Rui Shi, Wei Zeng, Zhengyu Su, Hanna Damasio, Zhonglin Lu, Yalin Wang, Shing-Tung Yau, Xianfeng Gu

Abstract: Automatic computation of surface correspondence via harmonic map is an active research field in computer vision, computer graphics and computational geometry. It may help document and understand physical and biological phenomena and also has broad applications in biometrics, medical imaging and motion capture. Although numerous studies have been devoted to harmonic map research, limited progress has been made to compute a diffeomorphic harmonic map on general topology surfaces with landmark constraints. This work conquer this problem by changing the Riemannian metric on the target surface to a hyperbolic metric, so that the harmonic mapping is guaranteed to be a diffeomorphism under landmark constraints. The computational algorithms are based on the Ricci flow method and the method is general and robust. We apply our algorithm to study constrained human brain surface registration problem. Experimental results demonstrate that, by changing the Riemannian metric, the registrations are always diffeomorphic, and achieve relative high performance when evaluated with some popular cortical surface registration evaluation standards.

@InProceedings{Shi_2013_ICCV_Workshops,author = {Shi, Rui and Zeng, Wei and Su, Zhengyu and Damasio, Hanna and Lu, Zhonglin and Wang, Yalin and Yau, Shing-Tung and Gu, Xianfeng},title = {Hyperbolic Harmonic Mapping for Constrained Brain Surface Registration},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: When modeling structured outputs such as image segmentations, prediction can be improved by accurately modeling structure present in the labels. A key challenge is developing tractable models that are able to capture complex high level structure like shape. In this work, we study the learning of a general class of pattern-like high order potential, which we call Compositional High Order Pattern Potentials (CHOPPs). We show that CHOPPs include the linear deviation pattern potentials of Rother et al. [26] and also Restricted Boltzmann Machines (RBMs); we also establish the near equivalence of these two models. Experimentally, we show that performance is affected significantly by the degree of variability present in the datasets, and we define a quantitative variability measure to aid in studying this. We then improve CHOPPs performance in high variability datasets with two primary contributions: (a) developing a loss-sensitive joint learning procedure, so that internal pattern parameters can be learned in conjunction with other model potentials to minimize expected loss;and (b) learning an image-dependent mapping that encourages or inhibits patterns depending on image features. We also explore varying how multiple patterns are composed, and learning convolutional patterns. Quantitative results on challenging highly variable datasets show that the joint learning and image-dependent high order potentials can improve performance.

@InProceedings{Li_2013_ICCV_Workshops,author = {Li, Yujia and Tarlow, Daniel and Zemel, Richard},title = {Exploring Compositional High Order Pattern Potentials for Structured Output Learning},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Ravi Garg, Anastasios Roussos, Lourdes Agapito

Abstract: This paper offers the first variational approach to the problem of dense 3D reconstruction of non-rigid surfaces from a monocular video sequence. We formulate nonrigid structure from motion ( NRS f M ) as a global variational energy minimization problem to estimate dense low-rank smooth 3D shapes for every frame along with the camera motion matrices, given dense 2D correspondences. Unlike traditional factorization based approaches to NRS f M , which model the low-rank non-rigid shape using a fixed number of basis shapes and corresponding coefficients, we minimize the rank of the matrix of time-varying shapes directly via trace norm minimization. In conjunction with this low-rank constraint, we use an edge preserving total-variation regularization term to obtain spatially smooth shapes for every frame. Thanks to proximal splitting techniques the optimization problem can be decomposed into many point-wise sub-problems and simple linear systems which can be easily solved on GPU hardware. We show results on real sequences of different objects (face, torso, beating heart) where, despite challenges in tracking, illumination changes and occlusions, our method reconstructs highly deforming smooth surfaces densely and accurately directly from video, without the need for any prior models or shape templates.

@InProceedings{Garg_2013_ICCV_Workshops,author = {Garg, Ravi and Roussos, Anastasios and Agapito, Lourdes},title = {Dense Variational Reconstruction of Non-rigid Surfaces from Monocular Video},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Dror Sholomon, Omid David, Nathan S. Netanyahu

Abstract: In this paper we propose the first effective automated, genetic algorithm (GA)-based jigsaw puzzle solver. We introduce a novel procedure of merging two "parent" solutions to an improved "child" solution by detecting, extracting, and combining correctly assembled puzzle segments. The solver proposed exhibits state-of-the-art performance solving previously attempted puzzles faster and far more accurately, and also puzzles of size never before attempted. Other contributions include the creation of a benchmark of large images, previously unavailable. We share the data sets and all of our results for future testing and comparative evaluation of jigsaw puzzle solvers.

@InProceedings{Sholomon_2013_ICCV_Workshops,author = {Sholomon, Dror and David, Omid and Netanyahu, Nathan S.},title = {A Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Jaechul Kim, Ce Liu, Fei Sha, Kristen Grauman

Abstract: We introduce a fast deformable spatial pyramid (DSP) matching algorithm for computing dense pixel correspondences. Dense matching methods typically enforce both appearance agreement between matched pixels as well as geometric smoothness between neighboring pixels. Whereas the prevailing approaches operate at the pixel level, we propose a pyramid graph model that simultaneously regularizes match consistency at multiple spatial extents--ranging from an entire image, to coarse grid cells, to every single pixel. This novel regularization substantially improves pixel-level matching in the face of challenging image variations, while the "deformable" aspect of our model overcomes the strict rigidity of traditional spatial pyramids. Results on LabelMe and Caltech show our approach outperforms state-of-the-art methods (SIFT Flow [15] and PatchMatch [2]), both in terms of accuracy and run time.

@InProceedings{Kim_2013_ICCV_Workshops,author = {Kim, Jaechul and Liu, Ce and Sha, Fei and Grauman, Kristen},title = {Deformable Spatial Pyramid Matching for Fast Dense Correspondences},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Yuichi Takeda, Shinsaku Hiura, Kosuke Sato

Abstract: In this paper we propose a novel depth measurement method by fusing depth from defocus (DFD) and stereo. One of the problems of passive stereo method is the difficulty of finding correct correspondence between images when an object has a repetitive pattern or edges parallel to the epipolar line. On the other hand, the accuracy of DFD method is inherently limited by the effective diameter of the lens. Therefore, we propose the fusion of stereo method and DFD by giving different focus distances for left and right cameras of a stereo camera with coded apertures. Two types of depth cues, defocus and disparity, are naturally integrated by the magnification and phase shift of a single point spread function (PSF) per camera. In this paper we give the proof of the proportional relationship between the diameter of defocus and disparity which makes the calibration easy. We also show the outstanding performance of our method which has both advantages of two depth cues through simulation and actual experiments.

@InProceedings{Takeda_2013_ICCV_Workshops,author = {Takeda, Yuichi and Hiura, Shinsaku and Sato, Kosuke},title = {Fusing Depth from Defocus and Stereo with Coded Apertures},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Roisin Rowley-Brooke, Francois Pitie, Anil Kokaram

Abstract: This paper presents recent work on a new framework for non-blind document bleed-through removal. The framework includes image preprocessing to remove local intensity variations, pixel region classification based on a segmentation of the joint recto-verso intensity histogram and connected component analysis on the subsequent image labelling. Finally restoration of the degraded regions is performed using exemplar-based image inpainting. The proposed method is evaluated visually and numerically on a freely available database of 25 scanned manuscript image pairs with ground truth, and is shown to outperform recent non-blind bleed-through removal techniques.

@InProceedings{Rowley-Brooke_2013_ICCV_Workshops,author = {Rowley-Brooke, Roisin and Pitie, Francois and Kokaram, Anil},title = {A Non-parametric Framework for Document Bleed-through Removal},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: Local spatio-temporal interest points (STIPs) and the resulting features from RGB videos have been proven successful at activity recognition that can handle cluttered backgrounds and partial occlusions. In this paper, we propose its counterpart in depth video and show its efficacy on activity recognition. We present a filtering method to extract STIPs from depth videos (called DSTIP) that effectively suppress the noisy measurements. Further, we build a novel depth cuboid similarity feature (DCSF) to describe the local 3D depth cuboid around the DSTIPs with an adaptable supporting size. We test this feature on activity recognition application using the public MSRAction3D, MSRDailyActivity3D datasets and our own dataset. Experimental evaluation shows that the proposed approach outperforms stateof-the-art activity recognition algorithms on depth videos, and the framework is more widely applicable than existing approaches. We also give detailed comparisons with other features and analysis of choice of parameters as a guidance for applications.

@InProceedings{Xia_2013_ICCV_Workshops,author = {Xia, Lu and Aggarwal, J.K.},title = {Spatio-temporal Depth Cuboid Similarity Feature for Activity Recognition Using Depth Camera},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


J. Kappes, B. Andres, F. Hamprecht, C. Schnorr, S. Nowozin, D. Batra, S. Kim, B. Kausler, J. Lellmann, N. Komodakis, C. Rother

Abstract: Seven years ago, Szeliski et al. published an influential study on energy minimization methods for Markov random fields (MRF). This study provided valuable insights in choosing the best optimization technique for certain classes of problems. While these insights remain generally useful today, the phenominal success of random field models means that the kinds of inference problems we solve have changed significantly. Specifically, the models today often include higher order interactions, flexible connectivity structures, large label-spaces of different cardinalities, or learned energy tables. To reflect these changes, we provide a modernized and enlarged study. We present an empirical comparison of 24 state-of-art techniques on a corpus of 2,300 energy minimization instances from 20 diverse computer vision applications. To ensure reproducibility, we evaluate all methods in the OpenGM2 framework and report extensive results regarding runtime and solution quality. Key insights from our study agree with the results of Szeliski et al. for the types of models they studied. However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types.

@InProceedings{Kappes_2013_ICCV_Workshops,author = {Kappes, J. and Andres, B. and Hamprecht, F. and Schnorr, C. and Nowozin, S. and Batra, D. and Kim, S. and Kausler, B. and Lellmann, J. and Komodakis, N. and Rother, C.},title = {A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Submodular Salient Region Detection

Abstract: The problem of salient region detection is formulated as the well-studied facility location problem from operations research. High-level priors are combined with low-level features to detect salient regions. Salient region detection is achieved by maximizing a submodular objective function, which maximizes the total similarities (i.e., total profits) between the hypothesized salient region centers (i.e., facility locations) and their region elements (i.e., clients), and penalizes the number of potential salient regions (i.e., the number of open facilities). The similarities are efficiently computed by finding a closed-form harmonic solution on the constructed graph for an input image. The saliency of a selected region is modeled in terms of appearance and spatial location. By exploiting the submodularity properties of the objective function, a highly efficient greedy-based optimization algorithm can be employed. This algorithm is guaranteed to be at least a (e 1)/e ? 0.632-approximation to the optimum. Experimental results demonstrate that our approach outperforms several recently proposed saliency detection approaches.

@InProceedings{Jiang_2013_ICCV_Workshops,author = {Jiang, Zhuolin and Davis, Larry S.},title = {Submodular Salient Region Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Dan Levi, Shai Silberstein, Aharon Bar-Hillel

Abstract: In this work we present a new part-based object detection algorithm with hundreds of parts performing realtime detection. Part-based models are currently state-ofthe-art for object detection due to their ability to represent large appearance variations. However, due to their high computational demands such methods are limited to several parts only and are too slow for practical real-time implementation. Our algorithm is an accelerated version of the "Feature Synthesis" (FS) method [1], which uses multiple object parts for detection and is among state-of-theart methods on human detection benchmarks, but also suffers from a high computational cost. The proposed Accelerated Feature Synthesis (AFS) uses several strategies for reducing the number of locations searched for each part. The first strategy uses a novel algorithm for approximate nearest neighbor search which we developed, termed "KDFerns", to compare each image location to only a subset of the model parts. Candidate part locations for a specific part are further reduced using spatial inhibition, and using an object-level "coarse-to-fine" strategy. In our empirical evaluation on pedestrian detection benchmarks, AFS maintains almost fully the accuracy performance of the original FS, while running more than x4 faster than existing partbased methods which use only several parts. AFS is to our best knowledge the first part-based object detection method achieving real-time running performance: nearly 10 frames per-second on 640 x 480 images on a regular CPU.

@InProceedings{Levi_2013_ICCV_Workshops,author = {Levi, Dan and Silberstein, Shai and Bar-Hillel, Aharon},title = {Fast Multiple-Part Based Object Detection Using KD-Ferns},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: Relating visual information to its linguistic semantic meaning remains an open and challenging area of research. The semantic meaning of images depends on the presence of objects, their attributes and their relations to other objects. But precisely characterizing this dependence requires extracting complex visual information from an image, which is in general a difficult and yet unsolved problem. In this paper, we propose studying semantic information in abstract images created from collections of clip art. Abstract images provide several advantages. They allow for the direct study of how to infer high-level semantic information, since they remove the reliance on noisy low-level object, attribute and relation detectors, or the tedious hand-labeling of images. Importantly, abstract images also allow the ability to generate sets of semantically similar scenes. Finding analogous sets of semantically similar real images would be nearly impossible. We create 1,002 sets of 10 semantically similar abstract scenes with corresponding written descriptions. We thoroughly analyze this dataset to discover semantically important features, the relations of words to visual features and methods for measuring semantic similarity.

@InProceedings{Zitnick_2013_ICCV_Workshops,author = {Zitnick, C. L. and Parikh, Devi},title = {Bringing Semantics into Focus Using Visual Abstraction},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Linjie Luo, Cha Zhang, Zhengyou Zhang, Szymon Rusinkiewicz

Abstract: We propose a novel algorithm to reconstruct the 3D geometry of human hairs in wide-baseline setups using strand-based refinement. The hair strands are first extracted in each 2D view, and projected onto the 3D visual hull for initialization. The 3D positions of these strands are then refined by optimizing an objective function that takes into account cross-view hair orientation consistency, the visual hull constraint and smoothness constraints defined at the strand, wisp and global levels. Based on the refined strands, the algorithm can reconstruct an approximate hair surface: experiments with synthetic hair models achieve an accuracy of ~3mm. We also show real-world examples to demonstrate the capability to capture full-head hair styles as well as hair in motion with as few as 8 cameras.

@InProceedings{Luo_2013_ICCV_Workshops,author = {Luo, Linjie and Zhang, Cha and Zhang, Zhengyou and Rusinkiewicz, Szymon},title = {Wide-Baseline Hair Capture Using Strand-Based Refinement},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Radial Distortion Self-Calibration

Jose Henrique Brito, Roland Angst, Kevin Koser, Marc Pollefeys

Abstract: In cameras with radial distortion, straight lines in space are in general mapped to curves in the image. Although epipolar geometry also gets distorted, there is a set of special epipolar lines that remain straight, namely those that go through the distortion center. By finding these straight epipolar lines in camera pairs we can obtain constraints on the distortion center(s) without any calibration object or plumbline assumptions in the scene. Although this holds for all radial distortion models we conceptually prove this idea using the division distortion model and the radial fundamental matrix which allow for a very simple closed form solution of the distortion center from two views (same distortion) or three views (different distortions). The non-iterative nature of our approach makes it immune to local minima and allows finding the distortion center also for cropped images or those where no good prior exists. Besides this, we give comprehensive relations between different undistortion models and discuss advantages and drawbacks.

@InProceedings{Brito_2013_ICCV_Workshops,author = {Henrique Brito, Jose and Angst, Roland and Koser, Kevin and Pollefeys, Marc},title = {Radial Distortion Self-Calibration},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: In this paper, we introduce a novel framework for computing a path of diffeomorphisms between a pair of input diffeomorphisms. Direct computation of a geodesic path on the space of diffeomorphisms Diff(?) is difficult, and it can be attributed mainly to the infinite dimensionality of Diff(?). Our proposed framework, to some degree, bypasses this difficulty using the quotient map of Diff(?) to the quotient space Diff(M )/Diff(M ) ? obtained by quotienting out the subgroup of volume-preserving diffeomorphisms Diff(M ) ? . This quotient space was recently identified as the unit sphere in a Hilbert space in mathematics literature, a space with well-known geometric properties. Our framework leverages this recent result by computing the diffeomorphic path in two stages. First, we project the given diffeomorphism pair onto this sphere and then compute the geodesic path between these projected points. Second, we lift the geodesic on the sphere back to the space of diffeomerphisms, by solving a quadratic programming problem with bilinear constraints using the augmented Lagrangian technique with penalty terms. In this way, we can estimate the path of diffeomorphisms, first, staying in the space of diffeomorphisms, and second, preserving shapes/volumes in the deformed images along the path as much as possible. We have applied our framework to interpolate intermediate frames of frame-sub-sampled video sequences. In the reported experiments, our approach compares favorably with the popular Large Deformation Diffeomorphic Metric Mapping framework (LDDMM).

@InProceedings{Seo_2013_ICCV_Workshops,author = {Seo, Dohyung and Ho, Jeffrey and Vemuri, Baba C.},title = {Computing Diffeomorphic Paths for Large Motion Interpolation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Maria Zontak, Inbar Mosseri, Michal Irani

Abstract: Recurrence of small clean image patches across different scales of a natural image has been successfully used for solving ill-posed problems in clean images (e.g., superresolution from a single image). In this paper we show how this multi-scale property can be extended to solve ill-posed problems under noisy conditions, such as image denoising. While clean patches are obscured by severe noise in the original scale of a noisy image, noise levels drop dramatically at coarser image scales. This allows for the unknown hidden clean patches to "naturally emerge" in some coarser scale of the noisy image. We further show that patch recurrence across scales is strengthened when using directional pyramids (that blur and subsample only in one direction). Our statistical experiments show that for almost any noisy image patch (more than 99%), there exists a "good" clean version of itself at the same relative image coordinates in some coarser scale of the image. This is a strong phenomenon of noise-contaminated natural images, which can serve as a strong prior for separating the signal from the noise. Finally, incorporating this multi-scale prior into a simple denoising algorithm yields state-of-the-art denoising results.

@InProceedings{Zontak_2013_ICCV_Workshops,author = {Zontak, Maria and Mosseri, Inbar and Irani, Michal},title = {Separating Signal from Noise Using Patch Recurrence across Scales},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Guang Chen, Yuanyuan Ding, Jing Xiao, Tony X. Han

Abstract: Context has been playing an increasingly important role to improve the object detection performance. In this paper we propose an effective representation, Multi-Order Contextual co-Occurrence (MOCO), to implicitly model the high level context using solely detection responses from a baseline object detector. The so-called (1 st -order) context feature is computed as a set of randomized binary comparisons on the response map of the baseline object detector. The statistics of the 1 st -order binary context features are further calculated to construct a high order co-occurrence descriptor. Combining the MOCO feature with the original image feature, we can evolve the baseline object detector to a stronger context aware detector. With the updated detector, we can continue the evolution till the contextual improvements saturate. Using the successful deformable-partmodel detector [13] as the baseline detector, we test the proposed MOCO evolution framework on the PASCAL VOC 2007 dataset [8] and Caltech pedestrian dataset [7]: The proposed MOCO detector outperforms all known state-ofthe-art approaches, contextually boosting deformable part models (ver.5) [13] by 3.3% in mean average precision on the PASCAL 2007 dataset. For the Caltech pedestrian dataset, our method further reduces the log-average miss rate from 48% to 46% and the miss rate at 1 FPPI from 2atfto m44%%compared with the best prior art [6].

@InProceedings{Chen_2013_ICCV_Workshops,author = {Chen, Guang and Ding, Yuanyuan and Xiao, Jing and Han, Tony X.},title = {Detection Evolution with Multi-order Contextual Co-occurrence},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: A Manhattan World (MW) [3] is composed of planar surfaces and parallel lines aligned with three mutually orthogonal principal axes. Traditional MW understanding algorithms rely on geometry priors such as the vanishing points and reference (ground) planes for grouping coplanar structures. In this paper, we present a novel single-image MW reconstruction algorithm from the perspective of nonpinhole cameras. We show that by acquiring the MW using an XSlit camera, we can instantly resolve coplanarity ambiguities. Specifically, we prove that parallel 3D lines map to 2D curves in an XSlit image and they converge at an XSlit Vanishing Point (XVP). In addition, if the lines are coplanar, their curved images will intersect at a second common pixel that we call Coplanar Common Point (CCP). CCP is a unique image feature in XSlit cameras that does not exist in pinholes. We present a comprehensive theory to analyze XVPs and CCPs in a MW scene and study how to recover 3D geometry in a complex MW scene from XVPs and CCPs. Finally, we build a prototype XSlit camera by using two layers of cylindrical lenses. Experimental results on both synthetic and real data show that our new XSlitcamera-based solution provides an effective and reliable solution for MW understanding.

@InProceedings{Ye_2013_ICCV_Workshops,author = {Ye, Jinwei and Ji, Yu and Yu, Jingyi},title = {Manhattan Scene Understanding via XSlit Imaging},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: We propose a novel nonparametric approach for semantic segmentation using high-order semantic relations. Conventional context models mainly focus on learning pairwise relationships between objects. Pairwise relations, however, are not enough to represent high-level contextual knowledge within images. In this paper, we propose semantic relation transfer, a method to transfer high-order semantic relations of objects from annotated images to unlabeled images analogous to label transfer techniques where label information are transferred. We first define semantic tensors representing high-order relations of objects. Semantic relation transfer problem is then formulated as semi-supervised learning using a quadratic objective function of the semantic tensors. By exploiting low-rank property of the semantic tensors and employing Kronecker sum similarity, an efficient approximation algorithm is developed. Based on the predicted high-order semantic relations, we reason semantic segmentation and evaluate the performance on several challenging datasets.

@InProceedings{Myeong_2013_ICCV_Workshops,author = {Myeong, Heesoo and Mu Lee, Kyoung},title = {Tensor-Based High-Order Semantic Relation Transfer for Semantic Scene Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Ke Chen, Shaogang Gong, Tao Xiang, Chen Change Loy

Abstract: A number of computer vision problems such as human age estimation, crowd density estimation and body/face pose (view angle) estimation can be formulated as a regression problem by learning a mapping function between a high dimensional vector-formed feature input and a scalarvalued output. Such a learning problem is made difficult due to sparse and imbalanced training data and large feature variations caused by both uncertain viewing conditions and intrinsic ambiguities between observable visual features and the scalar values to be estimated. Encouraged by the recent success in using attributes for solving classification problems with sparse training data, this paper introduces a novel cumulative attribute concept for learning a regression model when only sparse and imbalanced data are available. More precisely, low-level visual features extracted from sparse and imbalanced image samples are mapped onto a cumulative attribute space where each dimension has clearly defined semantic interpretation (a label) that captures how the scalar output value (e.g. age, people count) changes continuously and cumulatively. Extensive experiments show that our cumulative attribute framework gains notable advantage on accuracy for both age estimation and crowd counting when compared against conventional regression models, especially when the labelled training data is sparse with imbalanced sampling.

@InProceedings{Chen_2013_ICCV_Workshops,author = {Chen, Ke and Gong, Shaogang and Xiang, Tao and Change Loy, Chen},title = {Cumulative Attribute Space for Age and Crowd Density Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Hidekata Hontani, Yuto Tsunekawa, Yoshihide Sawada

Abstract: In this paper, we propose a new non-rigid robust registration method that registers a point distribution model (PDM) of a surface to given 3D images. The contributions of the paper are (1) a new hierarchical statistical shape model (SSM) of the surface that has better generalization ability is introduced, (2) the registration algorithm of the hierarchical SSM that can estimate the marginal posterior distribution of the surface location is proposed, and (3) the registration performance is improved by (3-1) robustly registering each local shape of the surface with the sparsity regularization and by (3-2) referring to the appearance between the neighboring model points in the likelihood computation. The SSM of a liver was constructed from a set of clinical CT images, and the performance of the proposed method was evaluated. Experimental results demonstrated that the proposed method outperformed some existing methods that use non-hierarchical SSMs.

@InProceedings{Hontani_2013_ICCV_Workshops,author = {Hontani, Hidekata and Tsunekawa, Yuto and Sawada, Yoshihide},title = {Accurate and Robust Registration of Nonrigid Surface Using Hierarchical Statistical Shape Model},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: From a set of images in a particular domain, labeled with part locations and class, we present a method to automatically learn a large and diverse set of highly discriminative intermediate features that we call Part-based One-vs-One Features (POOFs). Each of these features specializes in discrimination between two particular classes based on the appearance at a particular part. We demonstrate the particular usefulness of these features for fine-grained visual categorization with new state-of-the-art results on bird species identification using the Caltech UCSD Birds (CUB) dataset and parity with the best existing results in face verification on the Labeled Faces in the Wild (LFW) dataset. Finally, we demonstrate the particular advantage of POOFs when training data is scarce.

@InProceedings{Berg_2013_ICCV_Workshops,author = {Berg, Thomas and Belhumeur, Peter N.},title = {POOF: Part-Based One-vs.-One Features for Fine-Grained Categorization, Face Verification, and Attribute Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Xavier Boix, Michael Gygli, Gemma Roig, Luc Van Gool

Abstract: The representation of local image patches is crucial for the good performance and efficiency of many vision tasks. Patch descriptors have been designed to generalize towards diverse variations, depending on the application, as well as the desired compromise between accuracy and efficiency. We present a novel formulation of patch description, that serves such issues well. Sparse quantization lies at its heart. This allows for efficient encodings, leading to powerful, novel binary descriptors, yet also to the generalization of existing descriptors like SIFT or BRIEF. We demonstrate the capabilities of our formulation for both keypoint matching and image classification. Our binary descriptors achieve state-of-the-art results for two keypoint matching benchmarks, namely those by Brown [6] and Mikolajczyk [18]. For image classification, we propose new descriptors that perform similar to SIFT on Caltech101 [10] and PASCAL VOC07 [9].

@InProceedings{Boix_2013_ICCV_Workshops,author = {Boix, Xavier and Gygli, Michael and Roig, Gemma and Van Gool, Luc},title = {Sparse Quantization for Patch Description},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Yingying Zhu, Nandita M. Nayak, Amit K. Roy-Chowdhury

Abstract: In this paper, rather than modeling activities in videos individually, we propose a hierarchical framework that jointly models and recognizes related activities using motion and various context features. This is motivated from the observations that the activities related in space and time rarely occur independently and can serve as the context for each other. Given a video, action segments are automatically detected using motion segmentation based on a nonlinear dynamical model. We aim to merge these segments into activities of interest and generate optimum labels for the activities. Towards this goal, we utilize a structural model in a max-margin framework that jointly models the underlying activities which are related in space and time. The model explicitly learns the duration, motion and context patterns for each activity class, as well as the spatio-temporal relationships for groups of them. The learned model is then used to optimally label the activities in the testing videos using a greedy search method. We show promising results on the VIRAT Ground Dataset demonstrating the benefit of joint modeling and recognizing activities in a wide-area scene.

@InProceedings{Zhu_2013_ICCV_Workshops,author = {Zhu, Yingying and Nayak, Nandita M. and Roy-Chowdhury, Amit K.},title = {Context-Aware Modeling and Recognition of Activities in Video},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Carlos Arteta, Victor Lempitsky, J. A. Noble, Andrew Zisserman

Abstract: The objective of this work is to detect all instances of a class (such as cells or people) in an image. The instances may be partially overlapping and clustered, and hence quite challenging for traditional detectors, which aim at localizing individual instances. Our approach is to propose a set of candidate regions, and then select regions based on optimizing a global classification score, subject to the constraint that the selected regions are non-overlapping. Our novel contribution is to extend standard object detection by introducing separate classes for tuples of objects into the detection process. For example, our detector can pick a region containing two or three object instances, while assigning such region an appropriate label. We show that this formulation can be learned within the structured output SVM framework, and that the inference in such model can be accomplished using dynamic programming on a tree structured region graph. Furthermore, the learning only requires weak annotations a dot on each instance. The improvement resulting from the addition of the capability to detect tuples of objects is demonstrated on quite disparate data sets: fluorescence microscopy images and UCSD pedestrians.

@InProceedings{Arteta_2013_ICCV_Workshops,author = {Arteta, Carlos and Lempitsky, Victor and Noble, J. A. and Zisserman, Andrew},title = {Learning to Detect Partially Overlapping Instances},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Huizhong Chen, Andrew C. Gallagher, Bernd Girod

Abstract: This paper introduces a new idea in describing people using their first names, i.e., the name assigned at birth. We show that describing people in terms of similarity to a vector of possible first names is a powerful description of facial appearance that can be used for face naming and building facial attribute classifiers. We build models for 100 common first names used in the United States and for each pair, construct a pairwise firstname classifier. These classifiers are built using training images downloaded from the internet, with no additional user interaction. This gives our approach important advantages in building practical systems that do not require additional human intervention for labeling. We use the scores from each pairwise name classifier as a set of facial attributes. We show several surprising results. Our name attributes predict the correct first names of test faces at rates far greater than chance. The name attributes are applied to gender recognition and to age classification, outperforming state-of-the-art methods with all training images automatically gathered from the internet.

@InProceedings{Chen_2013_ICCV_Workshops,author = {Chen, Huizhong and Gallagher, Andrew C. and Girod, Bernd},title = {What's in a Name? First Names as Facial Attributes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Exemplar-Based Face Parsing

Brandon M. Smith, Li Zhang, Jonathan Brandt, Zhe Lin, Jianchao Yang

Abstract: In this work, we propose an exemplar-based face image segmentation algorithm. We take inspiration from previous works on image parsing for general scenes. Our approach assumes a database of exemplar face images, each of which is associated with a hand-labeled segmentation map. Given a test image, our algorithm first selects a subset of exemplar images from the database, Our algorithm then computes a nonrigid warp for each exemplar image to align it with the test image. Finally, we propagate labels from the exemplar images to the test image in a pixel-wise manner, using trained weights to modulate and combine label maps from different exemplars. We evaluate our method on two challenging datasets and compare with two face parsing algorithms and a general scene parsing algorithm. We also compare our segmentation results with contour-based face alignment results; that is, we first run the alignment algorithms to extract contour points and then derive segments from the contours. Our algorithm compares favorably with all previous works on all datasets evaluated.

@InProceedings{Smith_2013_ICCV_Workshops,author = {Smith, Brandon M. and Zhang, Li and Brandt, Jonathan and Lin, Zhe and Yang, Jianchao},title = {Exemplar-Based Face Parsing},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Tiezheng Ge, Kaiming He, Qifa Ke, Jian Sun

Abstract: Product quantization is an effective vector quantization approach to compactly encode high-dimensional vectors for fast approximate nearest neighbor (ANN) search. The essence of product quantization is to decompose the original high-dimensional space into the Cartesian product of a finite number of low-dimensional subspaces that are then quantized separately. Optimal space decomposition is important for the performance of ANN search, but still remains unaddressed. In this paper, we optimize product quantization by minimizing quantization distortions w.r.t. the space decomposition and the quantization codebooks. We present two novel methods for optimization: a nonparametric method that alternatively solves two smaller sub-problems, and a parametric method that is guaranteed to achieve the optimal solution if the input data follows some Gaussian distribution. We show by experiments that our optimized approach substantially improves the accuracy of product quantization for ANN search.

@InProceedings{Ge_2013_ICCV_Workshops,author = {Ge, Tiezheng and He, Kaiming and Ke, Qifa and Sun, Jian},title = {Optimized Product Quantization for Approximate Nearest Neighbor Search},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: Complex real-world signals, such as images, contain discriminative structures that differ in many aspects including scale, invariance, and data channel. While progress in deep learning shows the importance of learning features through multiple layers, it is equally important to learn features through multiple paths. We propose Multipath Hierarchical Matching Pursuit (M-HMP), a novel feature learning architecture that combines a collection of hierarchical sparse features for image classification to capture multiple aspects of discriminative structures. Our building blocks are MI-KSVD, a codebook learning algorithm that balances the reconstruction error and the mutual incoherence of the codebook, and batch orthogonal matching pursuit (OMP); we apply them recursively at varying layers and scales. The result is a highly discriminative image representation that leads to large improvements to the state-of-the-art on many standard benchmarks, e.g., Caltech-101, Caltech-256, MITScenes, Oxford-IIIT Pet and Caltech-UCSD Bird-200.

@InProceedings{Bo_2013_ICCV_Workshops,author = {Bo, Liefeng and Ren, Xiaofeng and Fox, Dieter},title = {Multipath Sparse Coding Using Hierarchical Matching Pursuit},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Shengfeng He, Qingxiong Yang, Rynson W.H. Lau, Jiang Wang, Ming-Hsuan Yang

@InProceedings{He_2013_ICCV_Workshops,author = {He, Shengfeng and Yang, Qingxiong and Lau, Rynson W.H. and Wang, Jiang and Yang, Ming-Hsuan},title = {Visual Tracking via Locality Sensitive Histograms},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Compressible Motion Fields

@InProceedings{Ottaviano_2013_ICCV_Workshops,author = {Ottaviano, Giuseppe and Kohli, Pushmeet},title = {Compressible Motion Fields},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


@InProceedings{Butt_2013_ICCV_Workshops,author = {Butt, Asad A. and Collins, Robert T.},title = {Multi-target Tracking by Lagrangian Relaxation to Min-cost Network Flow},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Tracking People and Their Objects

Tobias Baumgartner, Dennis Mitzel, Bastian Leibe

@InProceedings{Baumgartner_2013_ICCV_Workshops,author = {Baumgartner, Tobias and Mitzel, Dennis and Leibe, Bastian},title = {Tracking People and Their Objects},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


In Defense of 3D-Label Stereo

@InProceedings{Olsson_2013_ICCV_Workshops,author = {Olsson, Carl and Ulen, Johannes and Boykov, Yuri},title = {In Defense of 3D-Label Stereo},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Sid Yingze Bao, Manmohan Chandraker, Yuanqing Lin, Silvio Savarese

@InProceedings{Bao_2013_ICCV_Workshops,author = {Yingze Bao, Sid and Chandraker, Manmohan and Lin, Yuanqing and Savarese, Silvio},title = {Dense Object Reconstruction with Semantic Priors},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Deformable Graph Matching

Abstract: Graph matching (GM) is a fundamental problem in computer science, and it has been successfully applied to many problems in computer vision. Although widely used, existing GM algorithms cannot incorporate global consistence among nodes, which is a natural constraint in computer vision problems. This paper proposes deformable graph matching (DGM), an extension of GM for matching graphs subject to global rigid and non-rigid geometric constraints. The key idea of this work is a new factorization of the pair-wise affinity matrix. This factorization decouples the affinity matrix into the local structure of each graph and the pair-wise affinity edges. Besides the ability to incorporate global geometric transformations, this factorization offers three more benefits. First, there is no need to compute the costly (in space and time) pair-wise affinity matrix. Second, it provides a unified view of many GM methods and extends the standard iterative closest point algorithm. Third, it allows to use the path-following optimization algorithm that leads to improved optimization strategies and matching performance. Experimental results on synthetic and real databases illustrate how DGM outperforms state-of-the-art algorithms for GM. The code is available at http://humansensing.cs.cmu.edu/fgm .

@InProceedings{Zhou_2013_ICCV_Workshops,author = {Zhou, Feng and De la Torre, Fernando},title = {Deformable Graph Matching},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Ishani Chakraborty, Hui Cheng, Omar Javed

Abstract: We present a unified framework for detecting and classifying people interactions in unconstrained user generated images. g Unlike previous approaches that directly map people/face locations in 2D image space into features for classification, we first estimate camera viewpoint and people positions in 3D space and then extract spatial configuration features from explicit 3D people positions. This approach has several advantages. First, it can accurately estimate relative distances and orientations between people in 3D. Second, it encodes spatial arrangements of people into a richer set of shape descriptors than afforded in 2D. Our 3D shape descriptors are invariant to camera pose variations often seen in web images and videos. The proposed approach also estimates camera pose and uses it to capture the intent of the photo. To achieve accurate 3D people layout estimation, we develop an algorithm that robustly fuses semantic constraints about human interpositions into a linear camera model. This enables our model to handle large variations in people size, heights (e.g. age) and poses. An accurate 3D layout also allows us to construct features informed by Proxemics that improves our semantic classification. To characterize the human interaction space, we introduce visual proxemes; a set of prototypical patterns that represent commonly occurring social interactions in events. We train a discriminative classifier that classifies 3D arrangements of people into visual proxemes and quantitatively evaluate the performance on a large, challenging dataset.

@InProceedings{Chakraborty_2013_ICCV_Workshops,author = {Chakraborty, Ishani and Cheng, Hui and Javed, Omar},title = {3D Visual Proxemics: Recognizing Human Interactions in 3D from a Single Image},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: In this paper, we propose a graph-based method for 3D vessel tree structure segmentation based on a new tubularity Markov tree model (TMT ), which works as both new energy function and graph construction method. With the help of power-watershed implementation [7], a global optimal segmentation can be obtained with low computational cost. Different with other graph-based vessel segmentation methods, the proposed method does not depend on any skeleton and ROI extraction method. The classical issues of the graph-based methods, such as shrinking bias and sensitivity to seed point location, can be solved with the proposed method thanks to vessel data fidelity obtained with TMT . The proposed method is compared with some classical graph-based image segmentation methods and two up-to-date 3D vessel segmentation methods, and is demonstrated to be more accurate than these methods for 3D vessel tree segmentation. Although the segmentation is done without ROI extraction, the computational cost for the proposed method is low (within 20 seconds for 256*256*144 image).

@InProceedings{Zhu_2013_ICCV_Workshops,author = {Zhu, Ning and Chung, Albert C.S.},title = {Graph-Based Optimization with Tubularity Markov Tree for 3D Vessel Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Yi-Chen Chen, Vishal M. Patel, Jaishanker K. Pillai, Rama Chellappa, P. J. Phillips

Abstract: We propose a novel dictionary-based learning method for ambiguously labeled multiclass classification, where each training sample has multiple labels and only one of them is the correct label. The dictionary learning problem is solved using an iterative alternating algorithm. At each iteration of the algorithm, two alternating steps are performed: a confidence update and a dictionary update. The confidence of each sample is defined as the probability distribution on its ambiguous labels. The dictionaries are updated using either soft (EM-based) or hard decision rules. Extensive evaluations on existing datasets demonstrate that the proposed method performs significantly better than state-of-the-art ambiguously labeled learning approaches.

@InProceedings{Chen_2013_ICCV_Workshops,author = {Chen, Yi-Chen and Patel, Vishal M. and Pillai, Jaishanker K. and Chellappa, Rama and Phillips, P. J.},title = {Dictionary Learning from Ambiguously Labeled Data},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Aditya Khosla, Raffay Hamid, Chih-Jen Lin, Neel Sundaresan

Abstract: Given the enormous growth in user-generated videos, it is becoming increasingly important to be able to navigate them efficiently. As these videos are generally of poor quality, summarization methods designed for well-produced videos do not generalize to them. To address this challenge, we propose to use web-images as a prior to facilitate summarization of user-generated videos. Our main intuition is that people tend to take pictures of objects to capture them in a maximally informative way. Such images could therefore be used as prior information to summarize videos containing a similar set of objects. In this work, we apply our novel insight to develop a summarization algorithm that uses the web-image based prior information in an unsupervised manner. Moreover, to automatically evaluate summarization algorithms on a large scale, we propose a framework that relies on multiple summaries obtained through crowdsourcing. We demonstrate the effectiveness of our evaluation framework by comparing its performance to that of multiple human evaluators. Finally, we present results for our framework tested on hundreds of user-generated videos.

@InProceedings{Khosla_2013_ICCV_Workshops,author = {Khosla, Aditya and Hamid, Raffay and Lin, Chih-Jen and Sundaresan, Neel},title = {Large-Scale Video Summarization Using Web-Image Priors},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Yu-Tseh Chi, Mohsen Ali, Ajit Rajwade, Jeffrey Ho

Abstract: This paper proposes a dictionary learning framework that combines the proposed block/group (BGSC) or reconstructed block/group (R-BGSC) sparse coding schemes with the novel Intra-block Coherence Suppression Dictionary Learning (ICS-DL) algorithm. An important and distinguishing feature of the proposed framework is that all dictionary blocks are trained simultaneously with respect to each data group while the intra-block coherence being explicitly minimized as an important objective. We provide both empirical evidence and heuristic support for this feature that can be considered as a direct consequence of incorporating both the group structure for the input data and the block structure for the dictionary in the learning process. The optimization problems for both the dictionary learning and sparse coding can be solved efficiently using block-gradient descent, and the details of the optimization algorithms are presented. We evaluate the proposed methods using well-known datasets, and favorable comparisons with state-of-the-art dictionary learning methods demonstrate the viability and validity of the proposed framework.

@InProceedings{Chi_2013_ICCV_Workshops,author = {Chi, Yu-Tseh and Ali, Mohsen and Rajwade, Ajit and Ho, Jeffrey},title = {Block and Group Regularized Sparse Modeling for Dictionary Learning},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: We study the problem of part discovery when partial correspondence between instances of a category are available. For visual categories that exhibit high diversity in structure such as buildings, our approach can be used to discover parts that are hard to name, but can be easily expressed as a correspondence between pairs of images. Parts naturally emerge from point-wise landmark matches across many instances within a category. We propose a learning framework for automatic discovery of parts in such weakly supervised settings, and show the utility of the rich part library learned in this way for three tasks: object detection, category-specific saliency estimation, and fine-grained image parsing.

@InProceedings{Maji_2013_ICCV_Workshops,author = {Maji, Subhransu and Shakhnarovich, Gregory},title = {Part Discovery from Partial Correspondence},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Fast Convolutional Sparse Coding

Hilton Bristow, Anders Eriksson, Simon Lucey

Abstract: Sparse coding has become an increasingly popular method in learning and vision for a variety of classification, reconstruction and coding tasks. The canonical approach intrinsically assumes independence between observations during learning. For many natural signals however, sparse coding is applied to sub-elements ( i.e. patches) of the signal, where such an assumption is invalid. Convolutional sparse coding explicitly models local interactions through the convolution operator, however the resulting optimization problem is considerably more complex than traditional sparse coding. In this paper, we draw upon ideas from signal processing and Augmented Lagrange Methods (ALMs) to produce a fast algorithm with globally optimal subproblems and super-linear convergence.

@InProceedings{Bristow_2013_ICCV_Workshops,author = {Bristow, Hilton and Eriksson, Anders and Lucey, Simon},title = {Fast Convolutional Sparse Coding},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Alternating Decision Forests

Samuel Schulter, Paul Wohlhart, Christian Leistner, Amir Saffari, Peter M. Roth, Horst Bischof

Abstract: This paper introduces a novel classification method termed Alternating Decision Forests (ADFs), which formulates the training of Random Forests explicitly as a global loss minimization problem. During training, the losses are minimized via keeping an adaptive weight distribution over the training samples, similar to Boosting methods. In order to keep the method as flexible and general as possible, we adopt the principle of employing gradient descent in function space, which allows to minimize arbitrary losses. Contrary to Boosted Trees, in our method the loss minimization is an inherent part of the tree growing process, thus allowing to keep the benefits of common Random Forests, such as, parallel processing. We derive the new classifier and give a discussion and evaluation on standard machine learning data sets. Furthermore, we show how ADFs can be easily integrated into an object detection application. Compared to both, standard Random Forests and Boosted Trees, ADFs give better performance in our experiments, while yielding more compact models in terms of tree depth.

@InProceedings{Schulter_2013_ICCV_Workshops,author = {Schulter, Samuel and Wohlhart, Paul and Leistner, Christian and Saffari, Amir and Roth, Peter M. and Bischof, Horst},title = {Alternating Decision Forests},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Compressed Hashing

Yue Lin, Rong Jin, Deng Cai, Shuicheng Yan, Xuelong Li

Abstract: Recent studies have shown that hashing methods are effective for high dimensional nearest neighbor search. A common problem shared by many existing hashing methods is that in order to achieve a satisfied performance, a large number of hash tables (i.e., long codewords) are required. To address this challenge, in this paper we propose a novel approach called Compressed Hashing by exploring the techniques of sparse coding and compressed sensing. In particular, we introduce a sparse coding scheme, based on the approximation theory of integral operator, that generate sparse representation for high dimensional vectors. We then project sparse codes into a low dimensional space by effectively exploring the Restricted Isometry Property (RIP), a key property in compressed sensing theory. Both of the theoretical analysis and the empirical studies on two large data sets show that the proposed approach is more effective than the state-of-the-art hashing algorithms.

@InProceedings{Lin_2013_ICCV_Workshops,author = {Lin, Yue and Jin, Rong and Cai, Deng and Yan, Shuicheng and Li, Xuelong},title = {Compressed Hashing},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Yu Cao, Daniel Barrett, Andrei Barbu, Siddharth Narayanaswamy, Haonan Yu, Aaron Michaux, Yuewei Lin, Sven Dickinson, Jeffrey Mark Siskind, Song Wang

Abstract: Recognizing human activities in partially observed videos is a challenging problem and has many practical applications. When the unobserved subsequence is at the end of the video, the problem is reduced to activity prediction from unfinished activity streaming, which has been studied by many researchers. However, in the general case, an unobserved subsequence may occur at any time by yielding a temporal gap in the video. In this paper, we propose a new method that can recognize human activities from partially observed videos in the general case. Specifically, we formulate the problem into a probabilistic framework: 1) dividing each activity into multiple ordered temporal segments, 2) using spatiotemporal features of the training video samples in each segment as bases and applying sparse coding (SC) to derive the activity likelihood of the test video sample at each segment, and 3) finally combining the likelihood at each segment to achieve a global posterior for the activities. We further extend the proposed method to include more bases that correspond to a mixture of segments with different temporal lengths (MSSC), which can better represent the activities with large intra-class variations. We evaluate the proposed methods (SC and MSSC) on various real videos. We also evaluate the proposed methods on two special cases: 1) activity prediction where the unobserved subsequence is at the end of the video, and 2) human activity recognition on fully observed videos. Experimental results show that the proposed methods outperform existing state-of-the-art comparison methods.

@InProceedings{Cao_2013_ICCV_Workshops,author = {Cao, Yu and Barrett, Daniel and Barbu, Andrei and Narayanaswamy, Siddharth and Yu, Haonan and Michaux, Aaron and Lin, Yuewei and Dickinson, Sven and Mark Siskind, Jeffrey and Wang, Song},title = {Recognize Human Activities from Partially Observed Videos},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


SWIGS: A Swift Guided Sampling Method

Abstract: We present SWIGS, a Swift and efficient Guided Sampling method for robust model estimation from image feature correspondences. Our method leverages the accuracy of our new confidence measure (MR-Rayleigh), which assigns a correctness-confidence to a putative correspondence in an online fashion. MR-Rayleigh is inspired by Meta-Recognition (MR), an algorithm that aims to predict when a classifier's outcome is correct. We demonstrate that by using a Rayleigh distribution, the prediction accuracy of MR can be improved considerably. Our experiments show that MR-Rayleigh tends to predict better than the often-used Lowe's ratio, Brown's ratio, and the standard MR under a range of imaging conditions. Furthermore, our homography estimation experiment demonstrates that SWIGS performs similarly or better than other guided sampling methods while requiring fewer iterations, leading to fast and accurate model estimates.

@InProceedings{Fragoso_2013_ICCV_Workshops,author = {Fragoso, Victor and Turk, Matthew},title = {SWIGS: A Swift Guided Sampling Method},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Yale Song, Louis-Philippe Morency, Randall Davis

Abstract: Recent progress has shown that learning from hierarchical feature representations leads to improvements in various computer vision tasks. Motivated by the observation that human activity data contains information at various temporal resolutions, we present a hierarchical sequence summarization approach for action recognition that learns multiple layers of discriminative feature representations at different temporal granularities. We build up a hierarchy dynamically and recursively by alternating sequence learning and sequence summarization. For sequence learning we use CRFs with latent variables to learn hidden spatiotemporal dynamics; for sequence summarization we group observations that have similar semantic meaning in the latent space. For each layer we learn an abstract feature representation through non-linear gate functions. This procedure is repeated to obtain a hierarchical sequence summary representation. We develop an efficient learning method to train our model and show that its complexity grows sublinearly with the size of the hierarchy. Experimental results show the effectiveness of our approach, achieving the best published results on the ArmGesture and Canal9 datasets.

@InProceedings{Song_2013_ICCV_Workshops,author = {Song, Yale and Morency, Louis-Philippe and Davis, Randall},title = {Action Recognition by Hierarchical Sequence Summarization},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Liang Li, Wei Feng, Liang Wan, Jiawan Zhang

Abstract: This paper addresses a challenging problem of regularizing arbitrary superpixels into an optimal grid structure, which may significantly extend current low-level vision algorithms by allowing them to use superpixels (SPs) conveniently as using pixels. For this purpose, we aim at constructing maximum cohesive SP-grid, which is composed of real nodes, i.e. SPs, and dummy nodes that are meaningless in the image with only position-taking function in the grid. For a given formation of image SPs and proper number of dummy nodes, we first dynamically align them into a grid based on the centroid localities of SPs. We then define the SP-grid coherence as the sum of edge weights, with SP locality and appearance encoded, along all direct paths connecting any two nearest neighboring real nodes in the grid. We finally maximize the SP-grid coherence via cascade dynamic programming. Our approach can take the regional objectness as an optional constraint to produce more semantically reliable SP-grids. Experiments on object localization show that our approach outperforms state-of-the-art methods in terms of both detection accuracy and speed. We also find that with the same searching strategy and features, object localization at SP-level is about 100-500 times faster than pixel-level, with usually better detection accuracy.

@InProceedings{Li_2013_ICCV_Workshops,author = {Li, Liang and Feng, Wei and Wan, Liang and Zhang, Jiawan},title = {Maximum Cohesive Grid of Superpixels for Fast Object Localization},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: We propose a new convex regularizer, named the local color nuclear norm (LCNN), for color image recovery. The LCNN is designed to promote a property inherent in natural color images - in which their local color distributions often exhibit strong linearity - and is thus expected to reduce color artifact effectively. In addition, the very nature of LCNN allows us to incorporate it into various types of color image recovery formulations, with the associated convex optimization problems solvable using proximal splitting techniques. Applicatinos of LCNN are demonstrated with illustrative numerical examples.

@InProceedings{Ono_2013_ICCV_Workshops,author = {Ono, Shunsuke and Yamada, Isao},title = {A Convex Regularizer for Reducing Color Artifact in Color Image Recovery},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: We consider video object cut as an ensemble of framelevel background-foreground object classifiers which fuses information across frames and refine their segmentation results in a collaborative and iterative manner. Our approach addresses the challenging issues of modeling of background with dynamic textures and segmentation of foreground objects from cluttered scenes. We construct patch-level bagof-words background models to effectively capture the background motion and texture dynamics. We propose a foreground salience graph (FSG) to characterize the similarity of an image patch to the bag-of-words background models in the temporal domain and to neighboring image patches in the spatial domain. We incorporate this similarity information into a graph-cut energy minimization framework for foreground object segmentation. The background-foreground classification results at neighboring frames are fused together to construct a foreground probability map to update the graph weights. The resulting object shapes at neighboring frames are also used as constraints to guide the energy minimization process during graph cut. Our extensive experimental results and performance comparisons over a diverse set of challenging videos with dynamic scenes, including the new Change Detection Challenge Dataset, demonstrate that the proposed ensemble video object cut method outperforms various state-ofthe-art algorithms.

@InProceedings{Ren_2013_ICCV_Workshops,author = {Ren, Xiaobo and Han, Tony X. and He, Zhihai},title = {Ensemble Video Object Cut in Highly Dynamic Scenes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Peter Ochs, Alexey Dosovitskiy, Thomas Brox, Thomas Pock

Abstract: Natural image statistics indicate that we should use nonconvex norms for most regularization tasks in image processing and computer vision. Still, they are rarely used in practice due to the challenge to optimize them. Recently, iteratively reweighed 1 minimization has been proposed as a way to tackle a class of non-convex functions by solving a sequence of convex 2 1 problems. Here we extend the problem class to linearly constrained optimization of a Lipschitz continuous function, which is the sum of a convex function and a function being concave and increasing on the non-negative orthant (possibly non-convex and nonconcave on the whole space). This allows to apply the algorithm to many computer vision tasks. We show the effect of non-convex regularizers on image denoising, deconvolution, optical flow, and depth map fusion. Non-convexity is particularly interesting in combination with total generalized variation and learned image priors. Efficient optimization is made possible by some important properties that are shown to hold.

@InProceedings{Ochs_2013_ICCV_Workshops,author = {Ochs, Peter and Dosovitskiy, Alexey and Brox, Thomas and Pock, Thomas},title = {An Iterated L1 Algorithm for Non-smooth Non-convex Optimization in Computer Vision},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: Transparent gas flows are difficult to reconstruct: the refractive index field (RIF) within the gas volume is uneven and rapidly evolving, and correspondence matching under distortions is challenging. We present a novel computational imaging solution by exploiting the light field probe (LFProbe). A LF-probe resembles a view-dependent pattern where each pixel on the pattern maps to a unique ray. By observing the LF-probe through the gas flow, we acquire a dense set of ray-ray correspondences and then reconstruct their light paths. To recover the RIF, we use Fermat's Principle to correlate each light path with the RIF via a Partial Differential Equation (PDE). We then develop an iterative optimization scheme to solve for all light-path PDEs in conjunction. Specifically, we initialize the light paths by fitting Hermite splines to ray-ray correspondences, discretize their PDEs onto voxels, and solve a large, over-determined PDE system for the RIF. The RIF can then be used to refine the light paths. Finally, we alternate the RIF and light-path estimations to improve the reconstruction. Experiments on synthetic and real data show that our approach can reliably reconstruct small to medium scale gas flows. In particular, when the flow is acquired by a small number of cameras, the use of ray-ray correspondences can greatly improve the reconstruction.

@InProceedings{Ji_2013_ICCV_Workshops,author = {Ji, Yu and Ye, Jinwei and Yu, Jingyi},title = {Reconstructing Gas Flows Using Light-Path Approximation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: We propose a working set based approximate subgradient descent algorithm to minimize the margin-sensitive hinge loss arising from the soft constraints in max-margin learning frameworks, such as the structured SVM. We focus on the setting of general graphical models, such as loopy MRFs and CRFs commonly used in image segmentation, where exact inference is intractable and the most violated constraints can only be approximated, voiding the optimality guarantees of the structured SVM's cutting plane algorithm as well as reducing the robustness of existing subgradient based methods. We show that the proposed method obtains better approximate subgradients through the use of working sets, leading to improved convergence properties and increased reliability. Furthermore, our method allows new constraints to be randomly sampled instead of computed using the more expensive approximate inference techniques such as belief propagation and graph cuts, which can be used to reduce learning time at only a small cost of performance. We demonstrate the strength of our method empirically on the segmentation of a new publicly available electron microscopy dataset as well as the popular MSRC data set and show state-of-the-art results.

@InProceedings{Lucchi_2013_ICCV_Workshops,author = {Lucchi, Aurelien and Li, Yunpeng and Fua, Pascal},title = {Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Xiaoshuai Sun, Xin-Jing Wang, Hongxun Yao, Lei Zhang

Abstract: In this paper, we propose a computational model of visual representativeness by integrating cognitive theories of representativeness heuristics with computer vision and machine learning techniques. Unlike previous models that build their representativeness measure based on the visible data, our model takes the initial inputs as explicit positive reference and extend the measure by exploring the implicit negatives. Given a group of images that contains obvious visual concepts, we create a customized image ontology consisting of both positive and negative instances by mining the most related and confusable neighbors of the positive concept in ontological semantic knowledge bases. The representativeness of a new item is then determined by its likelihoods for both the positive and negative references. To ensure the effectiveness of probability inference as well as the cognitive plausibility, we discover the potential prototypes and treat them as an intermediate representation of semantic concepts. In the experiment, we evaluate the performance of representativeness models based on both human judgements and user-click logs of commercial image search engine. Experimental results on both ImageNet and image sets of general concepts demonstrate the superior performance of our model against the state-of-the-arts.

@InProceedings{Sun_2013_ICCV_Workshops,author = {Sun, Xiaoshuai and Wang, Xin-Jing and Yao, Hongxun and Zhang, Lei},title = {Exploring Implicit Image Statistics for Visual Representativeness Modeling},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Siddhartha Chandra, Shailesh Kumar, C.V. Jawahar

Abstract: Understanding the nature of data is the key to building good representations. In domains such as natural images, the data comes from very complex distributions which are hard to capture. Feature learning intends to discover or best approximate these underlying distributions and use their knowledge to weed out irrelevant information, preserving most of the relevant information. Feature learning can thus be seen as a form of dimensionality reduction. In this paper, we describe a feature learning scheme for natural images. We hypothesize that image patches do not all come from the same distribution, they lie in multiple nonlinear subspaces. We propose a framework that uses K Restricted Boltzmann Machines (K-RBM S ) to learn multiple non-linear subspaces in the raw image space. Projections of the image patches into these subspaces gives us features, which we use to build image representations. Our algorithm solves the coupled problem of finding the right non-linear subspaces in the input space and associating image patches with those subspaces in an iterative EM like algorithm to minimize the overall reconstruction error. Extensive empirical results over several popular image classification datasets show that representations based on our framework outperform the traditional feature representations such as the SIFT based Bag-of-Words (BoW) and convolutional deep belief networks.

@InProceedings{Chandra_2013_ICCV_Workshops,author = {Chandra, Siddhartha and Kumar, Shailesh and Jawahar, C.V.},title = {Learning Multiple Non-linear Sub-spaces Using K-RBMs},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Bastien Jacquet, Roland Angst, Marc Pollefeys

Abstract: Articulated objects represent an important class of objects in our everyday environment. Automatic detection of the type of articulated or otherwise restricted motion and extraction of the corresponding motion parameters are therefore of high value, e.g. in order to augment an otherwise static 3D reconstruction with dynamic semantics, such as rotation axes and allowable translation directions for certain rigid parts or objects. Hence, in this paper, a novel theory to analyse relative transformations between two motion-restricted parts will be presented. The analysis is based on linear subspaces spanned by relative transformations. Moreover, a signature for relative transformations will be introduced which uniquely specifies the type of restricted motion encoded in these relative transformations. This theoretic framework enables the derivation of novel algebraic constraints, such as low-rank constraints for subsequent rotations around two fixed axes for example. Lastly, given the type of restricted motion as predicted by the signature, the paper shows how to extract all the motion parameters with matrix manipulations from linear algebra. Our theory is verified on several real data sets, such as a rotating blackboard or a wheel rolling on the floor amongst others.

@InProceedings{Jacquet_2013_ICCV_Workshops,author = {Jacquet, Bastien and Angst, Roland and Pollefeys, Marc},title = {Articulated and Restricted Motion Subspaces and Their Signatures},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: Active learning provides useful tools to reduce annotation costs without compromising classifier performance. However it traditionally views the supervisor simply as a labeling machine. Recently a new interactive learning paradigm was introduced that allows the supervisor to additionally convey useful domain knowledge using attributes. The learner first conveys its belief about an actively chosen image e.g. "I think this is a forest, what do you think?". If the learner is wrong, the supervisor provides an explanation e.g. "No, this is too open to be a forest". With access to a pre-trained set of relative attribute predictors, the learner fetches all unlabeled images more open than the query image, and uses them as negative examples of forests to update its classifier. This rich human-machine communication leads to better classification performance. In this work, we propose three improvements over this set-up. First, we incorporate a weighting scheme that instead of making a hard decision reasons about the likelihood of an image being a negative example. Second, we do away with pre-trained attributes and instead learn the attribute models on the fly, alleviating overhead and restrictions of a pre-determined attribute vocabulary. Finally, we propose an active learning framework that accounts for not just the labelbut also the attributes-based feedback while selecting the next query image. We demonstrate significant improvement in classification accuracy on faces and shoes. We also collect and make available the largest relative attributes dataset containing 29 attributes of faces from 60 categories.

@InProceedings{Biswas_2013_ICCV_Workshops,author = {Biswas, Arijit and Parikh, Devi},title = {Simultaneous Active Learning of Classifiers & Attributes via Relative Feedback},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abed Malti, Richard Hartley, Adrien Bartoli, Jae-Hak Kim

Abstract: We propose a new approach for template-based extensible surface reconstruction from a single view. We extend the method of isometric surface reconstruction and more recent work on conformal surface reconstruction. Our approach relies on the minimization of a proposed stretching energy formalized with respect to the Poisson ratio parameter of the surface. We derive a patch-based formulation of this stretching energy by assuming local linear elasticity. This formulation unifies geometrical and mechanical constraints in a single energy term. We prevent local scale ambiguities by imposing a set of fixed boundary 3D points. We experimentally prove the sufficiency of this set of boundary points and demonstrate the effectiveness of our approach on different developable and non-developable surfaces with a wide range of extensibility.

@InProceedings{Malti_2013_ICCV_Workshops,author = {Malti, Abed and Hartley, Richard and Bartoli, Adrien and Kim, Jae-Hak},title = {Monocular Template-Based 3D Reconstruction of Extensible Surfaces with Local Linear Elasticity},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: We present a method to capture both 3D shape and spatially varying reflectance with a multi-view photometric stereo technique that works for general isotropic materials. Our data capture setup is simple, which consists of only a digital camera and a handheld light source. From a single viewpoint, we use a set of photometric stereo images to identify surface points with the same distance to the camera. We collect this information from multiple viewpoints and combine it with structure-from-motion to obtain a precise reconstruction of the complete 3D shape. The spatially varying isotropic bidirectional reflectance distribution function (BRDF) is captured by simultaneously inferring a set of basis BRDFs and their mixing weights at each surface point. According to our experiments, the captured shapes are accurate to 0.3 millimeters. The captured reflectance has relative root-mean-square error (RMSE) of 9%.

@InProceedings{Zhou_2013_ICCV_Workshops,author = {Zhou, Zhenglong and Wu, Zhe and Tan, Ping},title = {Multi-view Photometric Stereo with Spatially Varying Isotropic Materials},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Byung-Woo Hong, Zhaojin Lu, Ganesh Sundaramoorthi

Abstract: In this work, we address the multi-label Mumford-Shah problem, i.e., the problem of jointly estimating a partitioning of the domain of the image, and functions defined within regions of the partition. We create algorithms that are efficient, robust to undesirable local minima, and are easy-toimplement. Our algorithms are formulated by slightly modifying the underlying statistical model from which the multilabel Mumford-Shah functional is derived. The advantage of this statistical model is that the underlying variables: the labels and the functions are less coupled than in the original formulation, and the labels can be computed from the functions with more global updates. The resulting algorithms can be tuned to the desired level of locality of the solution: from fully global updates to more local updates. We demonstrate our algorithm on two applications: joint multi-label segmentation and denoising, and joint multi-label motion segmentation and flow estimation. We compare to the stateof-the-art in multi-label Mumford-Shah problems and show that we achieve more promising results.

@InProceedings{Hong_2013_ICCV_Workshops,author = {Hong, Byung-Woo and Lu, Zhaojin and Sundaramoorthi, Ganesh},title = {A New Model and Simple Algorithms for Multi-label Mumford-Shah Problems},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Raviteja Vemulapalli, Jaishanker K. Pillai, Rama Chellappa

Abstract: In computer vision applications, features often lie on Riemannian manifolds with known geometry. Popular learning algorithms such as discriminant analysis, partial least squares, support vector machines, etc., are not directly applicable to such features due to the non-Euclidean nature of the underlying spaces. Hence, classification is often performed in an extrinsic manner by mapping the manifolds to Euclidean spaces using kernels. However, for kernel based approaches, poor choice of kernel often results in reduced performance. In this paper, we address the issue of kernelselection for the classification of features that lie on Riemannian manifolds using the kernel learning approach. We propose two criteria for jointly learning the kernel and the classifier using a single optimization problem. Specifically, for the SVM classifier, we formulate the problem of learning a good kernel-classifier combination as a convex optimization problem and solve it efficiently following the multiple kernel learning approach. Experimental results on image set-based classification and activity recognition clearly demonstrate the superiority of the proposed approach over existing methods for classification of manifold features.

@InProceedings{Vemulapalli_2013_ICCV_Workshops,author = {Vemulapalli, Raviteja and Pillai, Jaishanker K. and Chellappa, Rama},title = {Kernel Learning for Extrinsic Classification of Manifold Features},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: This paper presents a system for image parsing, or labeling each pixel in an image with its semantic category, aimed at achieving broad coverage across hundreds of object categories, many of them sparsely sampled. The system combines region-level features with per-exemplar sliding window detectors. Per-exemplar detectors are better suited for our parsing task than traditional bounding box detectors: they perform well on classes with little training data and high intra-class variation, and they allow object masks to be transferred into the test image for pixel-level segmentation. The proposed system achieves state-of-theart accuracy on three challenging datasets, the largest of which contains 45,676 images and 232 labels.

@InProceedings{Tighe_2013_ICCV_Workshops,author = {Tighe, Joseph and Lazebnik, Svetlana},title = {Finding Things: Image Parsing with Regions and Per-Exemplar Detectors},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Ian Endres, Kevin J. Shih, Johnston Jiaa, Derek Hoiem

Abstract: We propose a method to learn a diverse collection of discriminative parts from object bounding box annotations. Part detectors can be trained and applied individually, which simplifies learning and extension to new features or categories. We apply the parts to object category detection, pooling part detections within bottom-up proposed regions and using a boosted classifier with proposed sigmoid weak learners for scoring. On PASCAL VOC 2010, we evaluate the part detectors' ability to discriminate and localize annotated keypoints. Our detection system is competitive with the best-existing systems, outperforming other HOG-based detectors on the more deformable categories.

@InProceedings{Endres_2013_ICCV_Workshops,author = {Endres, Ian and Shih, Kevin J. and Jiaa, Johnston and Hoiem, Derek},title = {Learning Collections of Part Models for Object Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Zhigang Ma, Yi Yang, Zhongwen Xu, Shuicheng Yan, Nicu Sebe, Alexander G. Hauptmann

Abstract: Complex events essentially include human, scenes, objects and actions that can be summarized by visual attributes, so leveraging relevant attributes properly could be helpful for event detection. Many works have exploited attributes at image level for various applications. However, attributes at image level are possibly insufficient for complex event detection in videos due to their limited capability in characterizing the dynamic properties of video data. Hence, we propose to leverage attributes at video level (named as video attributes in this work), i.e., the semantic labels of external videos are used as attributes. Compared to complex event videos, these external videos contain simple contents such as objects, scenes and actions which are the basic elements of complex events. Specifically, building upon a correlation vector which correlates the attributes and the complex event, we incorporate video attributes latently as extra informative cues into the event detector learnt from complex event videos. Extensive experiments on a real-world large-scale dataset validate the efficacy of the proposed approach.

@InProceedings{Ma_2013_ICCV_Workshops,author = {Ma, Zhigang and Yang, Yi and Xu, Zhongwen and Yan, Shuicheng and Sebe, Nicu and Hauptmann, Alexander G.},title = {Complex Event Detection via Multi-source Video Attributes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Yinda Zhang, Jianxiong Xiao, James Hays, Ping Tan

Abstract: We significantly extrapolate the field of view of a photograph by learning from a roughly aligned, wide-angle guide image of the same scene category. Our method can extrapolate typical photos into complete panoramas. The extrapolation problem is formulated in the shift-map image synthesis framework. We analyze the self-similarity of the guide image to generate a set of allowable local transformations and apply them to the input image. Our guided shift-map method preserves to the scene layout of the guide image when extrapolating a photograph. While conventional shiftmap methods only support translations, this is not expressive enough to characterize the self-similarity of complex scenes. Therefore we additionally allow image transformations of rotation, scaling and reflection. To handle this increase in complexity, we introduce a hierarchical graph optimization method to choose the optimal transformation at each output pixel. We demonstrate our approach on a variety of indoor, outdoor, natural, and man-made scenes.

@InProceedings{Zhang_2013_ICCV_Workshops,author = {Zhang, Yinda and Xiao, Jianxiong and Hays, James and Tan, Ping},title = {FrameBreak: Dramatic Image Extrapolation by Guided Shift-Maps},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: Imaging systems consisting of a camera looking at multiple spherical mirrors (reflection) or multiple refractive spheres (refraction) have been used for wide-angle imaging applications. We describe such setups as multi-axial imaging systems, since a single sphere results in an axial system. Assuming an internally calibrated camera, calibration of such multi-axial systems involves estimating the sphere radii and locations in the camera coordinate system. However, previous calibration approaches require manual intervention or constrained setups. We present a fully automatic approach using a single photo of a 2D calibration grid. The pose of the calibration grid is assumed to be unknown and is also recovered. Our approach can handle unconstrained setups, where the mirrors/refractive balls can be arranged in any fashion, not necessarily on a grid. The axial nature of rays allows us to compute the axis of each sphere separately. We then show that by choosing rays from two or more spheres, the unknown pose of the calibration grid can be obtained linearly and independently of sphere radii and locations. Knowing the pose, we derive analytical solutions for obtaining the sphere radius and location. This leads to an interesting result that 6-DOF pose estimation of a multi-axial camera can be done without the knowledge of full calibration. Simulations and real experiments demonstrate the applicability of our algorithm.

@InProceedings{Agrawal_2013_ICCV_Workshops,author = {Agrawal, Amit and Ramalingam, Srikumar},title = {Single Image Calibration of Multi-axial Imaging Systems},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: Within the fields of urban reconstruction and city modeling, shape grammars have emerged as a powerful tool for both synthesizing novel designs and reconstructing buildings. Traditionally, a human expert was required to write grammars for specific building styles, which limited the scope of method applicability. We present an approach to automatically learn two-dimensional attributed stochastic context-free grammars (2D-ASCFGs) from a set of labeled building facades. To this end, we use Bayesian Model Merging, a technique originally developed in the field of natural language processing, which we extend to the domain of two-dimensional languages. Given a set of labeled positive examples, we induce a grammar which can be sampled to create novel instances of the same building style. In addition, we demonstrate that our learned grammar can be used for parsing existing facade imagery. Experiments conducted on the dataset of Haussmannian buildings in Paris show that our parsing with learned grammars not only outperforms bottom-up classifiers but is also on par with approaches that use a manually designed style grammar.

@InProceedings{Martinovic_2013_ICCV_Workshops,author = {Martinovic, Andelo and Van Gool, Luc},title = {Bayesian Grammar Learning for Inverse Procedural Modeling},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Chunfeng Yuan, Xi Li, Weiming Hu, Haibin Ling, Stephen Maybank

Abstract: Spatio-temporal interest points serve as an elementary building block in many modern action recognition algorithms, and most of them exploit the local spatio-temporal volume features using a Bag of Visual Words (BOVW) representation. Such representation, however, ignores potentially valuable information about the global spatio-temporal distribution of interest points. In this paper, we propose a new global feature to capture the detailed geometrical distribution of interest points. It is calculated by using the R transform which is defined as an extended 3D discrete Radon transform, followed by applying a two-directional two-dimensional principal component analysis. Such R feature captures the geometrical information of the interest points and keeps invariant to geometry transformation and robust to noise. In addition, we propose a new fusion strategy to combine the R feature with the BOVW representation for further improving recognition accuracy. We utilize a context-aware fusion method to capture both the pairwise similarities and higher-order contextual interactions of the videos. Experimental results on several publicly available datasets demonstrate the effectiveness of the proposed approach for action recognition.

@InProceedings{Yuan_2013_ICCV_Workshops,author = {Yuan, Chunfeng and Li, Xi and Hu, Weiming and Ling, Haibin and Maybank, Stephen},title = {3D R Transform on Spatio-temporal Interest Points for Action Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: This paper discusses the problem of recognizing interaction-level human activities from a first-person viewpoint. The goal is to enable an observer (e.g., a robot or a wearable camera) to understand 'what activity others are performing to it' from continuous video inputs. These include friendly interactions such as 'a person hugging the observer' as well as hostile interactions like 'punching the observer' or 'throwing objects to the observer', whose videos involve a large amount of camera ego-motion caused by physical interactions. The paper investigates multichannel kernels to integrate global and local motion information, and presents a new activity learning/recognition methodology that explicitly considers temporal structures displayed in first-person activity videos. In our experiments, we not only show classification results with segmented videos, but also confirm that our new approach is able to detect activities from continuous videos reliably.

@InProceedings{Ryoo_2013_ICCV_Workshops,author = {Ryoo, Michael S. and Matthies, Larry},title = {First-Person Activity Recognition: What Are They Doing to Me?},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Abstract: With the increasing availability of high dimensional data and demand in sophisticated data analysis algorithms, manifold learning becomes a critical technique to perform dimensionality reduction, unraveling the intrinsic data structure. The real-world data however often come with noises and outliers; seldom, all the data live in a single linear subspace. Inspired by the recent advances in sparse subspace learning and diffusion-based approaches, we propose a new manifold denoising algorithm in which data neighborhoods are adaptively inferred via sparse subspace reconstruction; we then derive a new formulation to perform denoising to the original data. Experiments carried out on both toy and real applications demonstrate the effectiveness of our method; it is insensitive to parameter tuning and we show significant improvement over the competing algorithms.

@InProceedings{Wang_2013_ICCV_Workshops,author = {Wang, Bo and Tu, Zhuowen},title = {Sparse Subspace Denoising for Image Manifolds},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}


Jonghyun Choi, Mohammad Rastegari, Ali Farhadi, Larry S. Davis

Abstract: We propose a method to expand the visual coverage of training sets that consist of a small number of labeled examples using learned attributes. Our optimization formulation discovers category specific attributes as well as the images that have high confidence in terms of the attributes. In addition, we propose a method to stably capture example-specific attributes for a small sized training set. Our method adds images to a category from a large unlabeled image pool, and leads to significant improvement in category recognition accuracy evaluated on a large-scale dataset, ImageNet.

@InProceedings{Choi_2013_ICCV_Workshops,author = {Choi, Jonghyun and Rastegari, Mohammad and Farhadi, Ali and Davis, Larry S.},title = {Adding Unlabeled Samples to Categories by Learned Attributes},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}}