Jing Shao, Chen Change Loy, Xiaogang Wang

Abstract: Groups are the primary entities that make up a crowd. Understanding group-level dynamics and properties is thus scientifically important and practically useful in a wide range of applications, especially for crowd understanding. In this study we show that fundamental group-level properties, such as intra-group stability and inter-group conflict, can be systematically quantified by visual descriptors. This is made possible through learning a novel Collective Transition prior, which leads to a robust approach for group segregation in public spaces. From the prior, we further devise a rich set of group property visual descriptors. These descriptors are scene-independent, and can be effectively applied to public-scene with variety of crowd densities and distributions. Extensive experiments on hundreds of public scene video clips demonstrate that such property descriptors are not only useful but also necessary for group state analysis and crowd scene understanding.

@InProceedings{Shao_2014_CVPR,author = {Shao, Jing and Change Loy, Chen and Wang, Xiaogang},title = {Scene-Independent Group Profiling in Crowd},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Yu Cheng, Quanfu Fan, Sharath Pankanti, Alok Choudhary

Abstract: We present a novel approach for event detection in video by temporal sequence modeling. Exploiting temporal information has lain at the core of many approaches for video analysis (i.e., action, activity and event recognition). Unlike previous works doing temporal modeling at semantic event level, we propose to model temporal dependencies in the data at sub-event level without using event annotations. This frees our model from ground truth and addresses several limitations in previous work on temporal modeling. Based on this idea, we represent a video by a sequence of visual words learnt from the video, and apply the Sequence Memoizer [21] to capture long-range dependencies in a temporal context in the visual sequence. This data-driven temporal model is further integrated with event classification for jointly performing segmentation and classification of events in a video. We demonstrate the efficacy of our approach on two challenging datasets for visual recognition.

@InProceedings{Cheng_2014_CVPR,author = {Cheng, Yu and Fan, Quanfu and Pankanti, Sharath and Choudhary, Alok},title = {Temporal Sequence Modeling for Video Event Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Kuan-Ting Lai, Felix X. Yu, Ming-Syan Chen, Shih-Fu Chang

Abstract: Video event detection allows intelligent indexing of video content based on events. Traditional approaches extract features from video frames or shots, then quantize and pool the features to form a single vector representation for the entire video. Though simple and efficient, the final pooling step may lead to loss of temporally local information, which is important in indicating which part in a long video signifies presence of the event. In this work, we propose a novel instance-based video event detection approach. We represent each video as multiple "instances", defined as video segments of different temporal intervals. The objective is to learn an instance-level event detection model based on only video-level labels. To solve this problem, we propose a large-margin formulation which treats the instance labels as hidden latent variables, and simultaneously infers the instance labels as well as the instance-level classification model. Our framework infers optimal solutions that assume positive videos have a large number of positive instances while negative videos have the fewest ones. Extensive experiments on large-scale video event datasets demonstrate significant performance gains. The proposed method is also useful in explaining the detection results by localizing the temporal segments in a video which is responsible for the positive detection.

@InProceedings{Lai_2014_CVPR,author = {Lai, Kuan-Ting and Yu, Felix X. and Chen, Ming-Syan and Chang, Shih-Fu},title = {Video Event Detection by Inferring Temporal Instance Labels},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Subhabrata Bhattacharya, Mahdi M. Kalayeh, Rahul Sukthankar, Mubarak Shah

Abstract: While approaches based on bags of features excel at low-level action classification, they are ill-suited for recognizing complex events in video, where concept-based temporal representations currently dominate. This paper proposes a novel representation that captures the temporal dynamics of windowed mid-level concept detectors in order to improve complex event recognition. We first express each video as an ordered vector time series, where each time step consists of the vector formed from the concatenated confidences of the pre-trained concept detectors. We hypothesize that the dynamics of time series for different instances from the same event class, as captured by simple linear dynamical system (LDS) models, are likely to be  similar even if the instances differ in terms of low-level visual features. We propose a two-part representation composed of fusing: (1) a singular value decomposition of block Hankel matrices (SSID-S) and (2) a harmonic signature (HS) computed from the corresponding eigen-dynamics matrix. The proposed method offers several benefits over alternate approaches: our approach is straightforward to implement, directly employs existing concept detectors and can be plugged into linear classification frameworks. Results on standard datasets such as NIST's TRECVID Multimedia Event Detection task demonstrate the improved accuracy of the proposed method.

@InProceedings{Bhattacharya_2014_CVPR,author = {Bhattacharya, Subhabrata and Kalayeh, Mahdi M. and Sukthankar, Rahul and Shah, Mubarak},title = {Recognition of Complex Events: Exploiting Temporal Dynamics between Underlying Concepts},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Jaesik Park, Sudipta N. Sinha, Yasuyuki Matsushita, Yu-Wing Tai, In So Kweon

Abstract: We show that a non-isotropic near point light source rigidly attached to a camera can be calibrated using multiple images of a weakly textured planar scene. We prove that if the radiant intensity distribution (RID) of a light source is radially symmetric with respect to its dominant direction, then the shading observed on a Lambertian scene plane is bilaterally symmetric with respect to a 2D line on the plane. The symmetry axis detected in an image provides a linear constraint for estimating the dominant light axis. The light position and RID parameters can then be estimated using a linear method. Specular highlights if available can also be used for light position estimation. We also extend our method to handle non-Lambertian reflectances which we model using a biquadratic BRDF. We have evaluated our method on synthetic data quantitavely. Our experiments on real scenes show that our method works well in practice and enables light calibration without the need of a specialized hardware.

@InProceedings{Park_2014_CVPR,author = {Park, Jaesik and Sinha, Sudipta N. and Matsushita, Yasuyuki and Tai, Yu-Wing and So Kweon, In},title = {Calibrating a Non-isotropic Near Point Light Source using a Plane},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Chourmouzios Tsiotsios, Maria E. Angelopoulou, Tae-Kyun Kim, Andrew J. Davison

Abstract: Photometric stereo offers the possibility of object shape reconstruction via reasoning about the amount of light reflected from oriented surfaces. However, in murky media such as sea water, the illuminating light interacts with the medium and some of it is backscattered towards the camera. Due to this additive light component, the standard Photometric Stereo equations lead to poor quality shape estimation. Previous authors have attempted to reformulate the approach but have either neglected backscatter entirely or disregarded its non-uniformity on the sensor when camera and lights are close to each other. We show that by compensating effectively for the backscatter component, a linear formulation of Photometric Stereo is allowed which recovers an accurate normal map using only 3 lights. Our backscatter compensation method for point-sources can be used for estimating the uneven backscatter directly from single images without any prior knowledge about the characteristics of the medium or the scene. We compare our method with previous approaches through extensive experimental results, where a variety of objects are imaged in a big water tank whose turbidity is systematically increased, and show reconstruction quality which degrades little relative to clean water results even with a very significant scattering level.

@InProceedings{Tsiotsios_2014_CVPR,author = {Tsiotsios, Chourmouzios and Angelopoulou, Maria E. and Kim, Tae-Kyun and Davison, Andrew J.},title = {Backscatter Compensated Photometric Stereo with 3 Sources},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Rakesh Shiradkar, Li Shen, George Landon, Sim Heng Ong, Ping Tan

Abstract: The surface bi-directional reflectance distribution function (BRDF) can be used to distinguish different materials. The BRDFs of many real materials are near isotropic and can be approximated well by a 2D function. When the camera principal axis is coincident with the surface normal of the material sample, the captured BRDF slice is nearly 1D, which suffers from significant information loss. Thus, improvement in classification performance can be achieved by simply setting the camera at a slanted view to capture a larger portion of the BRDF domain. We further use a handheld flashlight camera to capture a 1D BRDF slice for material classification. This 1D slice captures important reflectance properties such as specular reflection and retro-reflectance. We apply these results on ink classification, which can be used in forensics and analyzing historical manuscripts. For the first time, we show that most of the inks on the market can be well distinguished by their reflectance properties.

@InProceedings{Shiradkar_2014_CVPR,author = {Shiradkar, Rakesh and Shen, Li and Landon, George and Heng Ong, Sim and Tan, Ping},title = {A New Perspective on Material Classification and Ink Identification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Sk. Mohammadul Haque, Avishek Chatterjee, Venu Madhav Govindu

Abstract: In this paper we present a depth-guided photometric 3D reconstruction method that works solely with a depth camera like the Kinect. Existing methods that fuse depth with normal estimates use an external RGB camera to obtain photometric information and treat the depth camera as a black box that provides a low quality depth estimate. Our contribution to such methods are two fold. Firstly, instead of using an extra RGB camera, we use the infra-red (IR) camera of the depth camera system itself to directly obtain high resolution photometric information. We believe that ours is the first method to use an IR depth camera system in this manner. Secondly, photometric methods applied to complex objects result in numerous holes in the reconstructed surface due to shadows and self-occlusions. To mitigate this problem, we develop a simple and effective multiview reconstruction approach that fuses depth and normal information from multiple viewpoints to build a complete, consistent and accurate 3D surface representation. We demonstrate the efficacy of our method to generate high quality 3D surface reconstructions for some complex 3D figurines.

@InProceedings{Haque_2014_CVPR,author = {Mohammadul Haque, Sk. and Chatterjee, Avishek and Madhav Govindu, Venu},title = {High Quality Photometric Reconstruction using a Depth Camera},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Hicham Badri, Hussein Yahia, Driss Aboutajdine

Abstract: Reconstructing a surface/image from corrupted gradient fields is a crucial step in many imaging applications where a gradient field  is subject to both noise and unlocalized outliers, resulting typically in a non-integrable field.  We present in this paper a new optimization method for robust surface reconstruction. The proposed formulation is based on a triple sparsity prior : a sparse prior on the residual gradient field and a double  sparse prior on the surface gradients. We develop an efficient alternate minimization strategy to solve the proposed optimization problem. The method is able to recover a good quality surface from severely corrupted gradients thanks to its ability to handle both noise and outliers. We demonstrate the performance of the proposed method on synthetic and real data. Experiments show that the proposed solution outperforms  some existing methods in the three possible cases : noise only, outliers only and mixed noise/outliers.

@InProceedings{Badri_2014_CVPR,author = {Badri, Hicham and Yahia, Hussein and Aboutajdine, Driss},title = {Robust Surface Reconstruction via Triple Sparsity},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Bo Dong, Kathleen D. Moore, Weiyi Zhang, Pieter Peers

Abstract: This paper proposes a novel photometric stereo solution to jointly estimate surface normals and scattering parameters from a globally planar, homogeneous, translucent object.  Similar to classic photometric stereo, our method only requires as few as three observations of the translucent object under directional lighting. Naively applying classic photometric stereo results in blurred photometric normals.  We develop a novel blind deconvolution algorithm based on inverse rendering for recovering the sharp surface normals and the material properties.  We demonstrate our method on a variety of translucent objects.

@InProceedings{Dong_2014_CVPR,author = {Dong, Bo and Moore, Kathleen D. and Zhang, Weiyi and Peers, Pieter},title = {Scattering Parameters and Surface Normals from Homogeneous Translucent Materials using Photometric Stereo},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Moumen T. El-Melegy, Aly S. Abdelrahim, Aly A. Farag

Abstract: The basic idea of shape from shading is to infer the shape of a surface from its shading information in a single image. Since this problem is ill-posed, a number of simplifying assumptions have been often used. However they rarely hold in practice. This paper presents a simple shading-correction algorithm that transforms the image to a new image that better satisfies the assumptions typically needed by existing algorithms, thus improving the accuracy of shape recovery. The algorithm takes advantage of some local shading measures that have been driven under these assumptions. The method is successfully evaluated on real data of human teeth with ground-truth 3D shapes.

@InProceedings{El-Melegy_2014_CVPR,author = {El-Melegy, Moumen T. and Abdelrahim, Aly S. and Farag, Aly A.},title = {Better Shading for Better Shape Recovery},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Nan Hu, Raif M. Rustamov, Leonidas Guibas

Abstract: In this paper, we consider the approximate weighted graph matching problem and introduce stable and informative first and second order compatibility terms suitable for inclusion into the popular integer quadratic program formulation. Our approach relies on a rigorous analysis of stability of spectral signatures based on the graph Laplacian. In the case of the first order term, we derive an objective function that measures both the stability and informativeness of a given spectral signature. By optimizing this objective, we design new spectral node signatures tuned to a specific graph to be matched. We also introduce the pairwise heat kernel distance as a stable second order compatibility term; we justify its plausibility by showing that in a certain limiting case it converges to the classical adjacency matrix-based second order compatibility function. We have tested our approach on a set of synthetic graphs, the widely-used CMU house sequence, and a set of real images. These experiments show the superior performance of our first and second order compatibility terms as compared with the commonly used ones.

@InProceedings{Hu_2014_CVPR,author = {Hu, Nan and Rustamov, Raif M. and Guibas, Leonidas},title = {Stable and Informative Spectral Signatures for Graph Matching},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Kangwei Liu, Junge Zhang, Kaiqi Huang, Tieniu Tan

Abstract: Deformable object matching, which is also called elastic matching or deformation matching, is an important and challenging problem in computer vision. Although numerous deformation models have been proposed in different matching tasks, not many of them investigate the intrinsic physics underlying deformation. Due to the lack of physical analysis, these models cannot describe the structure changes of deformable objects very well. Motivated by this, we analyze the deformation physically and propose a novel deformation decomposition model to represent various deformations. Based on the physical model, we formulate the matching problem as a two-mensional label Markov Random Field. The MRF energy function is derived from the deformation decomposition model. Furthermore, we propose a two-stage method to optimize the MRF energy function. To provide a quantitative benchmark, we build a deformation matching database with an evaluation criterion. Experimental results show that our method outperforms previous approaches especially on complex deformations.

@InProceedings{Liu_2014_CVPR,author = {Liu, Kangwei and Zhang, Junge and Huang, Kaiqi and Tan, Tieniu},title = {Deformable Object Matching via Deformation Decomposition based 2D Label MRF},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: Visual appearance score, appearance mixture type and deformation are three important information sources for human pose estimation. This paper proposes to build a multi-source deep model  in order to extract non-linear representation from these different aspects of information sources. With the deep model, the global, high-order human body articulation patterns in these information sources are extracted for pose estimation. The task for estimating body locations and the task for human detection are jointly learned using a unified deep model. The proposed approach can be viewed as a post-processing of pose estimation results and can flexibly integrate with existing methods by taking their information sources as input. By extracting the non-linear representation from multiple information sources, the deep model outperforms  state-of-the-art by up to 8.6 percent on three public benchmark datasets.

@InProceedings{Ouyang_2014_CVPR,author = {Ouyang, Wanli and Chu, Xiao and Wang, Xiaogang},title = {Multi-source Deep Learning for Human Pose Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: We present a simple vector quantizer that combines low distortion with fast search and apply it to approximate nearest neighbor (ANN) search in high dimensional spaces. Leveraging the very same data structure that is used to provide non-exhaustive search, i.e., inverted lists or a multi-index, the idea is to locally optimize an individual product quantizer (PQ) per cell and use it to encode residuals. Local optimization is over rotation and space decomposition; interestingly, we apply a parametric solution that assumes a normal distribution and is extremely fast to train. With a reasonable space and time overhead that is constant in the data size, we set a new state-of-the-art on several public datasets, including a billion-scale one.

@InProceedings{Kalantidis_2014_CVPR,author = {Kalantidis, Yannis and Avrithis, Yannis},title = {Locally Optimized Product Quantization for Approximate Nearest Neighbor Search},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Gerard Pons-Moll, David J. Fleet, Bodo Rosenhahn

Abstract: We advocate the inference of qualitative information about 3D human  pose, called posebits, from images.  Posebits represent boolean  geometric relationships between body parts  (e.g., left-leg in front of right-leg or hands close to each other). The advantages of posebits as a mid-level representation are 1)  for many tasks of interest, such qualitative pose information may be sufficient (e.g., semantic image retrieval),  2) it is relatively easy to annotate large image corpora with posebits, as  it simply requires answers to yes/no questions; and 3) they help  resolve challenging pose ambiguities and therefore facilitate the difficult talk of image-based 3D pose estimation. We introduce posebits, a posebit database, a method for selecting useful  posebits for pose estimation and a structural SVM model for posebit inference.   Experiments show the use of posebits for semantic image  retrieval and for improving 3D pose estimation.

@InProceedings{Pons-Moll_2014_CVPR,author = {Pons-Moll, Gerard and Fleet, David J. and Rosenhahn, Bodo},title = {Posebits for Monocular Human Pose Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: In this paper we present a novel real-time algorithm for simultaneous pose and shape estimation for articulated objects, such as human beings and animals. The key of our pose estimation component is to embed the articulated deformation model with exponential-maps-based parametrization into a Gaussian Mixture Model. Benefiting from the probabilistic measurement model, our algorithm requires no explicit point correspondences as opposed to most existing methods. Consequently, our approach is less sensitive to local minimum and well handles fast and complex motions. Extensive evaluations on publicly available datasets demonstrate that our method outperforms most state-of-art pose estimation algorithms with large margin, especially in the case of challenging motions. Moreover, our novel shape adaptation algorithm based on the same probabilistic model automatically captures the shape of the subjects during the dynamic pose estimation process. Experiments show that our shape estimation method achieves comparable accuracy with state of the arts, yet requires neither parametric model nor extra calibration procedure.

@InProceedings{Ye_2014_CVPR,author = {Ye, Mao and Yang, Ruigang},title = {Real-time Simultaneous Pose and Shape Estimation for Articulated Objects Using a Single Depth Camera},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Anoop Cherian, Julien Mairal, Karteek Alahari, Cordelia Schmid

Abstract: In this paper, we present a method for estimating articulated human poses in videos. We cast this as an optimization problem defined on body parts with spatio-temporal links between them. The resulting formulation is unfortunately intractable and previous approaches only provide approximate solutions. Although such methods perform well on certain body parts, e.g., head, their performance on lower arms, i.e., elbows and wrists, remains poor. We present a new approximate scheme with two steps dedicated to pose estimation. First, our approach takes into account temporal links with subsequent frames for the less-certain parts, namely elbows and wrists.  Second, our method decomposes poses into limbs, generates limb sequences across time, and recomposes poses by mixing these body part sequences.    We introduce a new dataset "Poses in the Wild", which is more challenging than the existing ones, with sequences containing background clutter, occlusions, and severe camera motion. We experimentally compare our method with recent approaches on this new dataset as well as on two other benchmark datasets, and show significant improvement.

@InProceedings{Cherian_2014_CVPR,author = {Cherian, Anoop and Mairal, Julien and Alahari, Karteek and Schmid, Cordelia},title = {Mixing Body-Part Sequences for Human Pose Estimation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Fisher and VLAD with FLAIR

Koen E. A. van de Sande, Cees G. M. Snoek, Arnold W. M. Smeulders

Abstract: A major computational bottleneck in many current algorithms is the evaluation of arbitrary boxes. Dense local analysis and powerful bag-of-word encodings, such as Fisher vectors and VLAD, lead to improved accuracy at the expense of increased computation time. Where a simplification in the representation is tempting, we exploit novel representations while maintaining accuracy. We start from state-of-the-art, fast selective search, but our method will apply to any initial box-partitioning. By representing the picture as sparse integral images, one per codeword, we achieve a Fast Local Area Independent Representation. FLAIR allows for very fast evaluation of any box encoding and still enables spatial pooling. In FLAIR we achieve exact VLAD's difference coding, even with L2 and power-norms. Finally, by multiple codeword assignments, we achieve exact and approximate Fisher vectors with FLAIR. The results are a 18x speedup, which enables us to set a new state-of-the-art on the challenging 2010 PASCAL VOC objects and the fine-grained categorization of the CUB-2011 200 bird species. Plus, we rank number one in the official ImageNet 2013 detection challenge.

@InProceedings{Sande_2014_CVPR,author = {van de Sande, Koen E. A. and Snoek, Cees G. M. and Smeulders, Arnold W. M.},title = {Fisher and VLAD with FLAIR},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Chunyu Wang, Yizhou Wang, Zhouchen Lin, Alan L. Yuille, Wen Gao

Abstract: Human pose estimation is a key step to action recognition. We propose a method of estimating 3D human poses from a single image, which works in conjunction with an existing 2D pose/joint detector. 3D pose estimation is challenging because multiple 3D poses may correspond to the same 2D pose after projection due to the lack of depth information. Moreover, current 2D pose estimators are usually inaccurate which may cause errors in the 3D estimation. We address the challenges in three ways: (i) We represent a 3D pose as a linear combination of a sparse set of bases learned from 3D human skeletons. (ii) We enforce limb length constraints to eliminate anthropomorphically implausible skeletons. (iii) We estimate a 3D pose by minimizing the L_1-norm error between the projection of the 3D pose and the corresponding 2D detection. The L_1-norm loss term is robust to inaccurate 2D joint estimations. We use the alternating direction method (ADM) to solve the optimization problem efficiently. Our approach outperforms the state-of-the-arts on three benchmark datasets.

@InProceedings{Wang_2014_CVPR,author = {Wang, Chunyu and Wang, Yizhou and Lin, Zhouchen and Yuille, Alan L. and Gao, Wen},title = {Robust Estimation of 3D Human Poses from a Single Image},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: The objective of this work is object category detection in large scale image datasets in the manner of Video Google — an object category is specified by a HOG classifier template, and retrieval is immediate at run time. We make the following three contributions: (i) a new image representation based on mid-level discriminative patches, that is designed to be suited to immediate object category detection and inverted file indexing; (ii) a sparse representation of a HOG classifier using a set of mid-level discriminative classifier patches; and (iii) a fast method for spatial reranking images on their detections. We evaluate the detection method on the standard PASCAL VOC 2007 dataset, together with a 100K image subset of ImageNet, and demonstrate near state of the art detection performance at low ranks whilst maintaining immediate retrieval speeds. Applications are also demonstrated using an exemplar-SVM for pose matched retrieval.

@InProceedings{Aytar_2014_CVPR,author = {Aytar, Yusuf and Zisserman, Andrew},title = {Immediate, Scalable Object Category Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: The presence of occluders significantly impacts performance of systems for object recognition.  However, occlusion is typically treated as an unstructured source of noise and explicit models for occluders have lagged behind those for object appearance and shape.  In this paper we describe a hierarchical deformable part model for face detection and keypoint localization that explicitly models occlusions of parts.  The proposed model structure makes it possible to augment positive training data with large numbers of synthetically occluded instances.  This allows us to easily incorporate the statistics of occlusion patterns in a discriminatively trained model.  We test the model on several benchmarks for keypoint localization including challenging sets featuring significant occlusion. We find that the addition of an explicit model of occlusion yields a system that outperforms existing approaches in keypoint localization accuracy.

@InProceedings{Ghiasi_2014_CVPR,author = {Ghiasi, Golnaz and Fowlkes, Charless C.},title = {Occlusion Coherence: Localizing Occluded Faces with a Hierarchical Deformable Part Model},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: Most pedestrian detection approaches that achieve high accuracy and precision rate and that can be used for real-time applications are based on histograms of gradient orientations. Usually multiscale detection is attained by resizing the image several times and by recomputing the image features or using multiple classifiers for different scales.  In this paper we present a pedestrian detection approach that uses the same classifier for all pedestrian scales based on image features computed for a single scale. We go beyond the low level pixel-wise gradient orientation bins and use higher level visual words organized into Word Channels. Boosting is used to learn classification features from the integral Word Channels.  The proposed approach is evaluated on multiple datasets and achieves outstanding results on the INRIA and Caltech-USA benchmarks. By using a GPU implementation we achieve a classification rate of over 10 million bounding boxes per second and a 16 FPS rate for multiscale detection in a 640×480 image.

@InProceedings{Costea_2014_CVPR,author = {Daniel Costea, Arthur and Nedevschi, Sergiu},title = {Word Channel Based Multiscale Pedestrian Detection Without Image Resizing and Using Only One Classifier},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Parsing Occluded People

Golnaz Ghiasi, Yi Yang, Deva Ramanan, Charless C. Fowlkes

Abstract: Occlusion poses a significant difficulty for object recognition due to the combinatorial diversity of possible occlusion patterns. We take a strongly supervised, non-parametric approach to modeling occlusion by learning deformable models with many local part mixture templates using large quantities of synthetically generated training data. This allows the model to learn the appearance of different occlusion patterns including figure-ground cues such as the shapes of occluding contours as well as the co-occurrence statistics of occlusion between neighboring parts.  The underlying part mixture-structure also allows the model to capture coherence of object support masks between neighboring parts and make compelling predictions of figure-ground-occluder segmentations.  We test the resulting model on human pose estimation under heavy occlusion and find it produces improved localization accuracy.

@InProceedings{Ghiasi_2014_CVPR,author = {Ghiasi, Golnaz and Yang, Yi and Ramanan, Deva and Fowlkes, Charless C.},title = {Parsing Occluded People},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Ramazan Gokberk Cinbis, Jakob Verbeek, Cordelia Schmid

Abstract: Object category localization is a challenging problem in computer vision. Standard supervised training requires bounding box annotations of object instances. This time-consuming annotation process is sidestepped in weakly supervised learning. In this case, the supervised information is restricted to binary labels that indicate the absence/presence of object instances in the image, without their locations. We follow a multiple-instance learning approach that iteratively trains the detector and infers the object locations in the positive training images. Our main contribution is a multi-fold multiple instance learning procedure, which prevents training from prematurely locking onto erroneous object locations. This procedure is particularly important when high-dimensional representations, such as the Fisher vectors, are used. We present a detailed experimental evaluation using the PASCAL VOC 2007 dataset. Compared to state-of-the-art weakly supervised detectors, our approach better localizes objects in the training images, which translates into improved detection performance.

@InProceedings{Cinbis_2014_CVPR,author = {Gokberk Cinbis, Ramazan and Verbeek, Jakob and Schmid, Cordelia},title = {Multi-fold MIL Training for Weakly Supervised Object Localization},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Pekka Rantalankila, Juho Kannala, Esa Rahtu

Abstract: We present a method for generating object segmentation proposals from groups of superpixels. The goal is to propose accurate segmentations for all objects of an image. The proposed object hypotheses can be used as input to object detection systems and thereby improve efficiency by replacing exhaustive search. The segmentations are generated in a class-independent manner and therefore the computational cost of the approach is independent of the number of object classes. Our approach combines both global and local search in the space of sets of superpixels. The local search is implemented by greedily merging adjacent pairs of superpixels to build a bottom-up segmentation hierarchy. The regions from such a hierarchy directly provide a part of our region proposals. The  global search provides the other part by performing a set of graph cut segmentations on a superpixel graph obtained from an intermediate level of the hierarchy. The parameters of the graph cut problems are learnt in such a manner that they provide complementary sets of regions. Experiments with Pascal VOC images show that we reach state-of-the-art with greatly reduced computational cost.

@InProceedings{Rantalankila_2014_CVPR,author = {Rantalankila, Pekka and Kannala, Juho and Rahtu, Esa},title = {Generating Object Segmentation Proposals using Global and Local Search},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: This paper proposes a novel mean field-based Chamfer template matching method. In our method, each template is represented as a field model and matching a template with an input image is formulated as estimation of a maximum of posteriori in the field model. Variational approach is then adopted to approximate the estimation. The proposed method was applied for two different variants of Chamfer template matching and evaluated through the task of object detection. Experimental results on benchmark datasets including ETHZShapeClass and INRIAHorse have shown that the proposed method could significantly improve the accuracy of template matching while not sacrificing much of the efficiency. Comparisons with other recent template matching algorithms have also shown the robustness of the proposed method.

@InProceedings{Nguyen_2014_CVPR,author = {Thanh Nguyen, Duc},title = {A Novel Chamfer Template Matching Method Using Variational Mean Field},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: Over the past years, Multiple Instance Learning (MIL) has proven to be an effective framework for learning with weakly labeled data. Applications of MIL to object detection, however, were limited to handling the uncertainties of manual annotations. In this paper, we propose a new MIL method for object detection that is capable of handling the noisier automatically obtained annotations. Our approach consists in first obtaining confidence estimates over the label space and, second, incorporating these estimates within a new Boosting procedure. We demonstrate the efficiency of our procedure on two detection tasks, namely, horse detection and pedestrian detection, where the training data is primarily annotated by a coarse area of interest detector. We show dramatic improvements over existing MIL methods. In both cases, we demonstrate that an efficient appearance model can be learned using our approach.

@InProceedings{Ali_2014_CVPR,author = {Ali, Karim and Saenko, Kate},title = {Confidence-Rated Multiple Instance Boosting for Object Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: We introduce a new approach for recognizing and reconstructing 3D objects in images. Our approach is based on an analysis by synthesis strategy. A forward synthesis model constructs possible geometric interpretations of the world, and then selects the interpretation that best agrees with the measured visual evidence. The forward model synthesizes visual templates defined on invariant (HOG) features. These visual templates are discriminatively trained to be accurate for inverse estimation. We introduce an efficient "brute-force" approach to inference that searches through a large number of candidate reconstructions, returning the optimal one. One benefit of such an approach is that recognition is inherently (re)constructive. We show state of the art performance for detection and reconstruction on two challenging 3D object recognition datasets of cars and cuboids.

@InProceedings{Hejrati_2014_CVPR,author = {Hejrati, Mohsen and Ramanan, Deva},title = {Analysis by Synthesis: 3D Object Recognition by Object Reconstruction},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Thomas Mensink, Efstratios Gavves, Cees G.M. Snoek

Abstract: In this paper we aim for zero-shot classification, that is visual recognition of an unseen class by using knowledge transfer from known classes. Our main contribution is COSTA, which exploits co-occurrences of visual concepts in images for knowledge transfer. These inter-dependencies arise naturally between concepts, and are easy to obtain from existing annotations or web-search hit counts. We estimate a classifier for a new label, as a weighted combination of related classes, using the co-occurrences to define the weight.	 We propose various metrics to leverage these co-occurrences, and a regression model for learning a weight for each related class. We also show that our zero-shot classifiers can serve as priors for few-shot learning. Experiments on three  multi-labeled datasets reveal that our proposed zero-shot methods, are approaching and occasionally outperforming fully supervised SVMs. We conclude that co-occurrence statistics suffice for zero-shot classification.

@InProceedings{Mensink_2014_CVPR,author = {Mensink, Thomas and Gavves, Efstratios and Snoek, Cees G.M.},title = {COSTA: Co-Occurrence Statistics for Zero-Shot Classification},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Submodular Object Recognition

Abstract: We present a novel object recognition framework based on multiple figure-ground hypotheses with a large object spatial support, generated by bottom-up processes and mid-level cues in an unsupervised manner. We exploit the benefit of regression for discriminating segments' categories and qualities, where a regressor is trained to each category using the overlapping observations between each figure-ground segment hypothesis and the ground-truth of the target category in an image. Object recognition is achieved by maximizing a submodular objective function, which maximizes the similarities between the selected segments (i.e., facility locations) and their group elements (i.e., clients), penalizes the number of selected segments, and more importantly, encourages the consistency of object categories corresponding to maximum regression values from different category-specific regressors for the selected segments. The proposed framework achieves impressive recognition results on three benchmark datasets, including PASCAL VOC 2007, Caltech-101 and ETHZ-shape.

@InProceedings{Zhu_2014_CVPR,author = {Zhu, Fan and Jiang, Zhuolin and Shao, Ling},title = {Submodular Object Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Kun Duan, David J. Crandall, Dhruv Batra

Abstract: Photo-sharing websites have become very popular in the last few years, leading to huge collections of online images. In addition to image data, these websites collect a variety of multimodal metadata about photos including text tags, captions, GPS coordinates, camera metadata, user profiles, etc. However, this metadata is not well constrained and is often noisy, sparse, or missing altogether. In this paper, we propose a framework to model these "loosely organized" multimodal datasets, and show how to perform loosely-supervised learning using a novel latent Conditional Random Field framework. We learn parameters of the LCRF automatically from a small set of validation data, using Information Theoretic Metric Learning (ITML) to learn distance functions and a structural SVM formulation to learn the potential functions. We apply our framework on four datasets of images from Flickr, evaluating both qualitatively and quantitatively against several baselines.

@InProceedings{Duan_2014_CVPR,author = {Duan, Kun and Crandall, David J. and Batra, Dhruv},title = {Multimodal Learning in Loosely-organized Web Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Generalized Max Pooling

Abstract: State-of-the-art patch-based image representations involve a pooling operation that aggregates statistics computed from local descriptors. Standard pooling operations include sum- and max-pooling. Sum-pooling lacks discriminability because the resulting representation is strongly influenced by frequent yet often uninformative descriptors, but only weakly influenced by rare yet potentially highly-informative ones. Max-pooling equalizes the influence of frequent and rare descriptors but is only applicable to representations that rely on count statistics, such as the bag-of-visual-words (BOV) and its soft- and sparse-coding extensions. We propose a novel pooling mechanism that achieves the same effect as max-pooling but is applicable beyond the BOV and especially to the state-of-the-art Fisher Vector-- hence the name Generalized Max Pooling (GMP). It involves equalizing the similarity between each patch and the pooled representation, which is shown to be equivalent to re-weighting the per-patch statistics. We show on five public image classification benchmarks that the proposed GMP can lead to significant performance gains with respect to heuristic alternatives.

@InProceedings{Murray_2014_CVPR,author = {Murray, Naila and Perronnin, Florent},title = {Generalized Max Pooling},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Mahsa Baktashmotlagh, Mehrtash T. Harandi, Brian C. Lovell, Mathieu Salzmann

Abstract: In this paper, we tackle the problem of unsupervised domain adaptation for classification. In the unsupervised scenario where no labeled samples from the target domain are provided, a popular approach consists in transforming the data such that the source and target distributions become similar. To compare the two distributions, existing approaches make use of the Maximum Mean Discrepancy (MMD). However, this does not exploit the fact that probability distributions lie on a Riemannian manifold. Here, we propose to make better use of the structure of this manifold and rely on the distance on the manifold to compare the source and target distributions. In this framework, we introduce a sample selection method and a subspace-based method for unsupervised domain adaptation, and show that both these manifold-based techniques outperform the corresponding approaches based on the MMD. Furthermore, we show that our subspace-based approach yields state-of-the-art results on a standard object recognition benchmark.

@InProceedings{Baktashmotlagh_2014_CVPR,author = {Baktashmotlagh, Mahsa and Harandi, Mehrtash T. and Lovell, Brian C. and Salzmann, Mathieu},title = {Domain Adaptation on the Statistical Manifold},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Junjie Yan, Zhen Lei, Longyin Wen, Stan Z. Li

Abstract: This paper solves the speed bottleneck of deformable part model (DPM), while maintaining the accuracy in detection on challenging datasets. Three prohibitive steps in cascade version of DPM are accelerated, including 2D correlation between root filter and feature map, cascade part pruning and HOG feature extraction. For 2D correlation, the root filter is constrained to be low rank, so that 2D correlation can be calculated by more efficient linear combination of 1D correlations. A proximal gradient algorithm is adopted to progressively learn the low rank filter in a discriminative manner. For cascade part pruning, neighborhood aware cascade is proposed to capture the dependence in neighborhood regions for aggressive pruning. Instead of explicit computation of part scores, hypotheses can be pruned by scores of neighborhoods under the first order approximation. For HOG feature extraction, look-up tables are constructed to replace expensive calculations of orientation partition and magnitude with simpler matrix index operations. Extensive experiments show that (a) the proposed method is 4 times faster than the current fastest DPM method with similar accuracy on Pascal VOC, (b) the proposed method achieves state-of-the-art accuracy on pedestrian and face detection task with frame-rate speed.

@InProceedings{Yan_2014_CVPR,author = {Yan, Junjie and Lei, Zhen and Wen, Longyin and Li, Stan Z.},title = {The Fastest Deformable Part Model for Object Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Christoph Goring, Erik Rodner, Alexander Freytag, Joachim Denzler

Abstract: In the following paper, we present an approach for fine-grained recognition based on a new part detection method. In particular, we propose a nonparametric label transfer technique which transfers part constellations from objects with similar global shapes. The possibility for transferring part annotations to unseen images allows for coping with a high degree of pose and view variations in scenarios where traditional detection models (such as deformable part models) fail. Our approach is especially valuable for fine-grained recognition scenarios where intraclass variations are extremely high, and precisely localized features need to be extracted.  Furthermore, we show the importance of carefully designed visual extraction strategies, such as combination of complementary feature types and iterative image segmentation, and the resulting impact on the recognition performance. In experiments, our simple yet powerful approach achieves 35.9% and 57.8% accuracy on the CUB-2010 and 2011 bird datasets, which is the current best performance for these benchmarks.

@InProceedings{Goring_2014_CVPR,author = {Goring, Christoph and Rodner, Erik and Freytag, Alexander and Denzler, Joachim},title = {Nonparametric Part Transfer for Fine-grained Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Jifeng Dai, Yi Hong, Wenze Hu, Song-Chun Zhu, Ying Nian Wu

Abstract: This paper proposes an unsupervised method for learning dictionaries of hierarchical compositional models for representing natural images. Each model is in the form of a template that consists of a small group of part templates that are allowed to shift their locations and orientations relative to each other, and each part template is in turn a composition of Gabor wavelets that are also allowed to shift their locations and orientations relative to each other. Given a set of unannotated training images, a dictionary of such hierarchical templates are learned so that each training image can be represented by a small number of templates that are spatially translated, rotated and scaled versions of the templates in the learned dictionary. The learning algorithm iterates between the following two steps: (1) Image encoding by a template matching pursuit process that involves a bottom-up template matching sub-process and a top-down template localization sub-process. (2) Dictionary re-learning by a shared matching pursuit process. Experimental results show that the proposed approach is capable of learning meaningful templates, and the learned templates are useful for tasks such as domain adaption and image cosegmentation.

@InProceedings{Dai_2014_CVPR,author = {Dai, Jifeng and Hong, Yi and Hu, Wenze and Zhu, Song-Chun and Nian Wu, Ying},title = {Unsupervised Learning of Dictionaries of Hierarchical Compositional Models},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: With the widespread availability of video cameras, we are facing an ever-growing enormous collection of unedited and unstructured video data. Due to lack of an automatic way to generate summaries from this large collection of consumer videos, they can be tedious and time consuming to index or search. In this work, we propose online video highlighting, a principled way of generating short video summarizing the most important and interesting contents of an unedited and unstructured video, costly both time-wise and financially for manual processing. Specifically, our method learns a dictionary from given video using group sparse coding, and updates atoms in the dictionary on-the-fly. A summary video is then generated by combining segments that cannot be sparsely reconstructed using the learned dictionary. The online fashion of our proposed method enables it to process arbitrarily long videos and start generating summaries before seeing the end of the video. Moreover, the processing time required by our proposed method is close to the original video length, achieving quasi real-time summarization speed. Theoretical analysis, together with experimental results on more than 12 hours of surveillance and YouTube videos are provided, demonstrating the effectiveness of online video highlighting.

@InProceedings{Zhao_2014_CVPR,author = {Zhao, Bin and Xing, Eric P.},title = {Quasi Real-Time Summarization for Consumer Videos},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Gait Recognition under Speed Transition

Al Mansur, Yasushi Makihara, Rasyid Aqmar, Yasushi Yagi

Abstract: This paper describes a method of gait recognition from image sequences wherein a subject is accelerating or decelerating. As a speed change occurs due to a change of pitch (the first-order derivative of a phase, namely, a gait stance) and/or stride, we model this speed change using a cylindrical manifold whose azimuth and height corresponds to the phase and the stride, respectively. A radial basis function (RBF) interpolation framework is used to learn subject specific mapping matrices for mapping from manifold to image space. Given an input image sequence of speed transited gait of a test subject, we estimate the mapping matrix of the test subject as well as the phase and stride sequence using an energy minimization framework considering the following three points: (1) fitness of the synthesized images to the input image sequence as well as to an eigenspace constructed by exemplars of training subjects; (2) smoothness of the phase and the stride sequence; and (3) pitch and stride fitness to the pitch-stride preference model. Using the estimated mapping matrix, we synthesize a constant-speed gait image sequence, and extract a conventional period-based gait feature from it for matching. We conducted experiments using real speed transited gait image sequences with 179 subjects and demonstrated the effectiveness of the proposed method.

@InProceedings{Mansur_2014_CVPR,author = {Mansur, Al and Makihara, Yasushi and Aqmar, Rasyid and Yagi, Yasushi},title = {Gait Recognition under Speed Transition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: The use of wearable cameras makes it possible to record life logging egocentric videos. Browsing such long unstructured videos is time consuming and tedious. Segmentation into meaningful chapters is an important first step towards adding structure to egocentric videos, enabling efficient browsing, indexing and summarization of the long videos. Two sources of information for video segmentation are (i) the motion of the camera wearer, and (ii) the objects and activities recorded in the video. In this paper we address the motion cues for video segmentation.  Motion based segmentation is especially difficult in egocentric videos when the camera is constantly moving due to natural head movement of the wearer. We propose a robust temporal segmentation of egocentric videos into a hierarchy of motion classes using a new Cumulative Displacement Curves. Unlike instantaneous motion vectors, segmentation using integrated motion vectors performs well even in dynamic and crowded scenes. No assumptions are made on the underlying scene structure and the method works in indoor as well as outdoor situations. We demonstrate the effectiveness of our approach using publicly available videos as well as choreographed videos. We also suggest an approach to detect the fixation of wearer's gaze in the walking portion of the egocentric videos.

@InProceedings{Poleg_2014_CVPR,author = {Poleg, Yair and Arora, Chetan and Peleg, Shmuel},title = {Temporal Segmentation of Egocentric Videos},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Shayan Modiri Assari, Amir Roshan Zamir, Mubarak Shah

Abstract: We address the problem of classifying complex videos based on their content. A typical approach to this problem is performing the classification using semantic attributes, commonly termed concepts, which occur in the video. In this paper, we propose a contextual approach to video classification based on Generalized Maximum Clique Problem (GMCP) which uses the co-occurrence of concepts as the context model. To be more specific, we propose to represent a class based on the co-occurrence of its concepts and classify a video based on matching its semantic co-occurrence pattern to each class representation. We perform the matching using GMCP which finds the strongest clique of co-occurring concepts in a video. We argue that, in principal, the co-occurrence of concepts yields a richer representation of a video compared to most of the current approaches. Additionally, we propose a novel optimal solution to GMCP based on Mixed Binary Integer Programming (MBIP). The evaluations show our approach, which opens new opportunities for further research in this direction, outperforms several well established video classification methods.

@InProceedings{Assari_2014_CVPR,author = {Modiri Assari, Shayan and Roshan Zamir, Amir and Shah, Mubarak},title = {Video Classification using Semantic Concept Co-occurrences},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Dan Oneata, Jakob Verbeek, Cordelia Schmid

Abstract: The Fisher vector (FV) representation is a high-dimensional extension of the popular bag-of-word representation. Transformation of the FV by power and L2 normalizations has shown to significantly improve its performance, and led to state-of-the-art results for a range of image and video classification and  retrieval tasks. These normalizations, however, render the representation non-additive over local descriptors. Combined with its high dimensionality, this makes the FV computationally expensive for the purpose of localization tasks. In this paper we present approximations to both these normalizations, which yield significant improvements in the memory and computational costs of the FV when used for localization. Second, we show how these approximations can be used to define upper-bounds on the score function that can be efficiently evaluated, which enables the use of branch-and-bound search as an alternative to exhaustive sliding window search. We present experimental evaluation results on  classification and temporal localization of actions in videos. These show that the our approximations lead to a speedup of at least one order of magnitude, while maintaining state-of-the-art action recognition and localization performance.

@InProceedings{Oneata_2014_CVPR,author = {Oneata, Dan and Verbeek, Jakob and Schmid, Cordelia},title = {Efficient Action Localization with Approximately Normalized Fisher Vectors},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Brais Cancela, Alberto Iglesias, Marcos Ortega, Manuel G. Penedo

Abstract: This paper presents a novel methodology for modelling pedestrian trajectories over a scene, based in the hypothesis that, when people try to reach a destination, they use the path that takes less time, taking into account environmental information like the type of terrain or what other people did before. Thus, a minimal path approach can be used to model human trajectory behaviour. We develop a modified Fast Marching Method that allows us to include both velocity and orientation in the Front Propagation Approach, without increasing its computational complexity. Combining all the information, we create a time surface that shows the time a target need to reach any given position in the scene. We also create different metrics in order to compare the time surface against the real behaviour. Experimental results over a public dataset prove the initial hypothesis' correctness.

@InProceedings{Cancela_2014_CVPR,author = {Cancela, Brais and Iglesias, Alberto and Ortega, Marcos and Penedo, Manuel G.},title = {Unsupervised Trajectory Modelling using Temporal Information via Minimal Paths},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: Due to great challenges such as tremendous intra-class variations and low image resolution, context information has been playing a more and more important role for accurate and robust event recognition in surveillance videos. The context information can generally be divided into the feature level context, the semantic level context, and the prior level context. These three levels of context provide crucial bottom-up, middle level, and top down information that can benefit the recognition task itself. Unlike existing researches that generally integrate the context information at one of the three levels, we propose a hierarchical context model that simultaneously exploits contexts at all three levels and systematically incorporate them into event recognition. To tackle the learning and inference challenges brought in by the model hierarchy, we develop complete learning and inference algorithms for the proposed hierarchical context model based on variational Bayes method. Experiments on VIRAT 1.0 and 2.0 Ground Datasets demonstrate the effectiveness of the proposed hierarchical context model for improving the event recognition performance even under great challenges like large intra-class variations and low image resolution.

@InProceedings{Wang_2014_CVPR,author = {Wang, Xiaoyang and Ji, Qiang},title = {A Hierarchical Context Model for Event Recognition in Surveillance Video},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: We propose a unified framework DISCOVER to simultaneously discover important segments, classify high-level events and generate recounting for large amounts of unconstrained web videos. The motivation is our observation that many video events are characterized by certain important segments. Our goal is to find the important segments and capture their information for event classification and recounting. We introduce an evidence localization model where evidence locations are modeled as latent variables. We impose constraints on global video appearance, local evidence appearance and the temporal structure of the evidence. The model is learned via a max-margin framework and allows efficient inference. Our method does not require annotating sources of evidence, and is jointly optimized for event classification and recounting. Experimental results are shown on the challenging TRECVID 2013 MEDTest dataset.

@InProceedings{Sun_2014_CVPR,author = {Sun, Chen and Nevatia, Ram},title = {DISCOVER: Discovering Important Segments for Classification of Video Events and Recounting},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: High dimensional representations such as VLAD or FV have shown excellent accuracy in action recognition. This paper shows that a proper encoding built upon VLAD can achieve further accuracy boost with only negligible computational cost. We empirically evaluated various VLAD improvement technologies to determine good practices in VLAD-based video encoding. Furthermore, we propose an interpretation that VLAD is a maximum entropy linear feature learning process. Combining this new perspective with observed VLAD data distribution properties, we propose a simple, lightweight, but powerful bimodal encoding method. Evaluated on 3 benchmark action recognition datasets (UCF101, HMDB51 and Youtube), the bimodal encoding improves VLAD by large margins in action recognition.

@InProceedings{Wu_2014_CVPR,author = {Wu, Jianxin and Zhang, Yu and Lin, Weiyao},title = {Towards Good Practices for Action Video Encoding},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Afshin Dehghan, Haroon Idrees, Mubarak Shah

Abstract: A video captures a sequence and interactions of concepts that can be static, for instance, objects or scenes, or dynamic, such as actions. For large datasets containing hundreds of thousands of images or videos, it is impractical to manually annotate all the concepts, or all the instances of a single concept. However, a dictionary with visuallydistinct elements can be created automatically from unlabeled videos which can capture and express the entire dataset. The downside to this machine-discovered dictionary is meaninglessness, i.e., its elements are devoid of semantics and interpretation. In this paper, we present an approach that leverages the strengths of semantic concepts and the machine-discovered DOVE by learning a relationship between them. Since instances of a semantic concept share visual similarity, the proposed approach uses softconsensus regularization to learn the mapping that enforces instances from each semantic concept to have similar representations. The testing is performed by projecting the query onto the DOVE as well as new representations of semantic concepts from training, with non-negativity and unit summation constraints for probabilistic interpretation. We tested our formulation on TRECVID MED and SIN tasks, and obtained encouraging results.

@InProceedings{Dehghan_2014_CVPR,author = {Dehghan, Afshin and Idrees, Haroon and Shah, Mubarak},title = {Improving Semantic Concept Detection through the Dictionary of Visually-distinct Elements},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Ankur Gupta, Julieta Martinez, James J. Little, Robert J. Woodham

Abstract: We describe a new approach to transfer knowledge across views for action recognition by using examples from a large collection of unlabelled mocap data. We achieve this by directly matching purely motion based features from videos to mocap. Our approach recovers 3D pose sequences without performing any body part tracking. We use these matches to generate multiple motion projections and thus add view invariance to our action recognition model. We also introduce a closed form solution for approximate non-linear Circulant Temporal Encoding (nCTE), which allows us to efficiently perform the matches in the frequency domain. We test our approach on the challenging unsupervised modality of the IXMAS dataset, and use publicly available motion capture data for matching. Without any additional annotation effort, we are able to significantly outperform the current state of the art.

@InProceedings{Gupta_2014_CVPR,author = {Gupta, Ankur and Martinez, Julieta and Little, James J. and Woodham, Robert J.},title = {3D Pose from Motion for Cross-view Action Recognition via Non-linear Circulant Temporal Encoding},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: Local video features provide state-of-the-art performance for action recognition. While the accuracy of action recognition has been continuously improved over the recent years, the low speed of feature extraction and subsequent recognition prevents current methods from scaling up to real-size problems. We address this issue and first develop highly efficient video features using motion information in video compression. We next explore feature encoding by Fisher vectors and demonstrate accurate action recognition using fast linear classifiers. Our method improves the speed of video feature extraction, feature encoding and action classification by two orders of magnitude at the cost of minor reduction in recognition accuracy. We validate our approach and compare it to the state of the art on four recent action recognition datasets.

@InProceedings{Kantorov_2014_CVPR,author = {Kantorov, Vadim and Laptev, Ivan},title = {Efficient Feature Extraction, Encoding and Classification for Action Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: Graphs are a powerful tool to model structured objects, but it is nontrivial to measure the similarity between two graphs. In this paper, we construct a two-graph model to represent human actions by recording the spatial and temporal relationships among local features. We also propose a novel family of context-dependent graph kernels (CGKs) to measure similarity between graphs. First, local features are used as the vertices of the two-graph model and the relationships among local features in the intra-frames and inter-frames are characterized by the edges. Then, the proposed CGKs are applied to measure the similarity between actions represented by the two-graph model. Graphs can be decomposed into numbers of primary walk groups with different walk lengths and our CGKs are based on the context-dependent primary walk group matching. Taking advantage of the context information makes the correctly matched primary walk groups dominate in the CGKs and improves the performance of similarity measurement between graphs. Finally, a generalized multiple kernel learning algorithm with a proposed l12-norm regularization is applied to combine these CGKs optimally together and simultaneously train a set of action classifiers. We conduct a series of experiments on several public action datasets. Our approach achieves a comparable performance to the state-of-the-art approaches, which demonstrates the effectiveness of the two-graph model and the CGKs in recognizing human actions.

@InProceedings{Wu_2014_CVPR,author = {Wu, Baoxin and Yuan, Chunfeng and Hu, Weiming},title = {Human Action Recognition Based on Context-Dependent Graph Kernels},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Yen-Yu Lin, Ju-Hsuan Hua, Nick C. Tang, Min-Hung Chen, Hong-Yuan Mark Liao

Abstract: The recent advances in RGB-D cameras have allowed us to better solve increasingly complex computer vision tasks. However, modern RGB-D cameras are still restricted by the short effective distances. The limitation may make RGB-D cameras not online accessible in practice, and degrade their applicability. We propose an alternative scenario to address this problem, and illustrate it with the application to action recognition. We use Kinect to offline collect an auxiliary, multi-modal database, in which not only the RGB videos but also the depth maps and skeleton structures of actions of interest are available. Our approach aims to enhance action recognition in RGB videos by leveraging the extra database. Specifically, it optimizes a feature transformation, by which the actions to be recognized can be concisely reconstructed by entries in the auxiliary database. In this way, the inter-database variations are adapted. More importantly, each action can be augmented with additional depth and skeleton images retrieved from the auxiliary database. The proposed approach has been evaluated on three benchmarks of action recognition. The promising results manifest that the augmented depth and skeleton features can lead to remarkable boost in recognition accuracy.

@InProceedings{Lin_2014_CVPR,author = {Lin, Yen-Yu and Hua, Ju-Hsuan and Tang, Nick C. and Chen, Min-Hung and Mark Liao, Hong-Yuan},title = {Depth and Skeleton Associated Action Recognition without Online Accessible RGB-D Cameras},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Lin Sun, Kui Jia, Tsung-Han Chan, Yuqiang Fang, Gang Wang, Shuicheng Yan

Abstract: Most of the previous work on video action recognition use complex hand-designed local features, such as SIFT, HOG and SURF, but these approaches are implemented sophisticatedly and difficult to be extended to other sensor modalities. Recent studies discover that there are no universally best hand-engineered features for all datasets, and learning features directly from the data may be more advantageous. One such endeavor is Slow Feature Analysis (SFA) proposed by Wiskott and Sejnowski. SFA can learn the invariant and slowly varying features from input signals and has been proved to be valuable in human action recognition. It is also observed that the multi-layer feature representation has succeeded remarkably in widespread machine learning applications. In this paper, we propose to combine SFA with deep learning techniques to learn hierarchical representations from the video data itself. Specifically, we use a two-layered SFA learning structure with 3D convolution and max pooling operations to scale up the method to large inputs and capture abstract and structural features from the video. Thus, the proposed method is suitable for action recognition. At the same time, sharing the same merits of deep learning, the proposed method is generic and fully automated. Our classification results on Hollywood2, KTH and UCF Sports are competitive with previously published results. To highlight some, on the KTH dataset, our recognition rate shows approximately 1% improvement in comparison to state-of-the-art methods even without supervision or dense sampling.

@InProceedings{Sun_2014_CVPR,author = {Sun, Lin and Jia, Kui and Chan, Tsung-Han and Fang, Yuqiang and Wang, Gang and Yan, Shuicheng},title = {DL-SFA: Deeply-Learned Slow Feature Analysis for Action Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Sanath Narayan, Kalpathi R. Ramakrishnan

Abstract: An action is typically composed of different parts of the object moving in particular sequences. The presence of different motions (represented as a 1D histogram) has been used in the traditional bag-of-words (BoW) approach for recognizing actions. However the interactions among the motions also form a crucial part of an action. Different object-parts have varying degrees of interactions with the other parts during an action cycle. It is these interactions we want to quantify in order to bring in additional information about the actions. In this paper we propose a causality based approach for quantifying the interactions to aid action classification. Granger causality is used to compute the cause and effect relationships for pairs of motion trajectories of a video. A 2D histogram descriptor for the video is constructed using these pairwise measures. Our proposed method of obtaining pairwise measures for videos is also applicable for large datasets. We have conducted experiments on challenging action recognition databases such as HMDB51 and UCF50 and shown that our causality descriptor helps in encoding additional information regarding the actions and performs on par with the state-of-the art approaches. Due to the complementary nature, a further increase in performance can be observed by combining our approach with state-of-the-art approaches.

@InProceedings{Narayan_2014_CVPR,author = {Narayan, Sanath and Ramakrishnan, Kalpathi R.},title = {A Cause and Effect Analysis of Motion Trajectories for Modeling Actions},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: We propose a probabilistic method for parsing a temporal sequence such as a complex activity defined as composition of sub-activities/actions. The temporal structure of the high-level activity is represented by a string-length limited stochastic context-free grammar. Given the grammar, a Bayes network, which we term Sequential Interval Network (SIN), is generated where the variable nodes correspond to the start and end times of component actions. The network integrates information about the duration of each primitive action, visual detection results for each primitive action, and the activity's temporal structure. At any moment in time during the activity, message passing is used to perform exact inference yielding the posterior probabilities of the start and end times for each different activity/action. We provide demonstrations of this framework being applied to vision tasks such as action prediction, classification of the high-level activities or temporal segmentation of a test sequence; the method is also applicable in Human Robot Interaction domain where continual prediction of human action is needed.

@InProceedings{Vo_2014_CVPR,author = {Vo, Nam N. and Bobick, Aaron F.},title = {From Stochastic Grammar to Bayes Network: Probabilistic Parsing of Complex Activity},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Jiang Wang, Xiaohan Nie, Yin Xia, Ying Wu, Song-Chun Zhu

Abstract: Existing methods on video-based action recognition are generally view-dependent, i.e., performing recognition from the same views seen in the training data. We present a novel multiview spatio-temporal AND-OR graph (MST-AOG) representation for  cross-view action recognition, i.e., the recognition is performed on the video from an unknown and unseen view. As a compositional model, MST-AOG compactly represents the hierarchical combinatorial structures of cross-view actions by explicitly modeling the geometry, appearance and motion variations. This paper proposes effective methods to learn the structure and parameters of MST-AOG. The inference based on MST-AOG enables action recognition from novel views. The training of MST-AOG takes advantage of the 3D human skeleton data obtained from Kinect cameras to avoid annotating enormous multi-view video frames, which is error-prone and time-consuming, but the recognition does not need 3D information and is based on 2D video input. A new Multiview Action3D dataset has been created and will be released. Extensive experiments have demonstrated that this new action representation significantly improves the accuracy and robustness for cross-view action recognition on 2D videos.

@InProceedings{Wang_2014_CVPR,author = {Wang, Jiang and Nie, Xiaohan and Xia, Yin and Wu, Ying and Zhu, Song-Chun},title = {Cross-view Action Modeling, Learning and Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Dahua Lin, Sanja Fidler, Chen Kong, Raquel Urtasun

Abstract: In this paper, we tackle the problem of retrieving videos using complex natural language queries. Towards this goal, we first parse the sentential descriptions into a semantic graph, which is then matched to visual concepts using a generalized bipartite matching algorithm.  Our approach exploits object appearance, motion and spatial relations, and learns the importance of each term using structure prediction.  We demonstrate the effectiveness of our approach on a new dataset designed  for semantic search in the context of autonomous driving, which exhibits complex and highly dynamic scenes with many objects. We show that our approach is able to locate a major portion of the objects described in the query with high accuracy, and improve the relevance in video retrieval.

@InProceedings{Lin_2014_CVPR,author = {Lin, Dahua and Fidler, Sanja and Kong, Chen and Urtasun, Raquel},title = {Visual Semantic Search: Retrieving Videos via Complex Textual Queries},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Shuang Wu, Sravanthi Bondugula, Florian Luisier, Xiaodan Zhuang, Pradeep Natarajan

Abstract: Current state-of-the-art systems for visual content analysis require large training sets for each class of interest, and performance degrades rapidly with fewer examples. In this paper, we present a general framework for the zeroshot learning problem of performing high-level event detection with no training exemplars, using only textual descriptions. This task goes beyond the traditional zero-shot framework of adapting a given set of classes with training data to unseen classes. We leverage video and image collections with free-form text descriptions from widely available web sources to learn a large bank of concepts, in addition to using several off-the-shelf concept detectors, speech, and video text for representing videos. We utilize natural language processing technologies to generate event description features. The extracted features are then projected to a common high-dimensional space using text expansion, and similarity is computed in this space. We present extensive experimental results on the large TRECVID MED corpus to demonstrate our approach. Our results show that the proposed concept detection methods significantly outperform current attribute classifiers such as Classemes, ObjectBank, and SUN attributes. Further, we find that fusion, both within as well as between modalities, is crucial for optimal performance.

@InProceedings{Wu_2014_CVPR,author = {Wu, Shuang and Bondugula, Sravanthi and Luisier, Florian and Zhuang, Xiaodan and Natarajan, Pradeep},title = {Zero-shot Event Detection using Multi-modal Fusion of Weakly Supervised Concepts},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Christoph Feichtenhofer, Axel Pinz, Richard P. Wildes

Abstract: This paper presents a unified bag of visual word (BoW) framework for dynamic scene recognition. The approach builds on primitive features that uniformly capture spatial and temporal orientation structure of the imagery (e.g., video), as extracted via application of a bank of spatiotemporally oriented filters. Various feature encoding techniques are investigated to abstract the primitives to an intermediate representation that is best suited to dynamic scene representation. Further, a novel approach to adaptive pooling of the encoded features is presented that captures spatial layout of the scene even while being robust to situations where camera motion and scene dynamics are confounded. The resulting overall approach has been evaluated on two standard, publically available dynamic scene datasets. The results show that in comparison to a representative set of alternatives, the proposed approach outperforms the previous state-of-the-art in classification accuracy by 10%.

@InProceedings{Feichtenhofer_2014_CVPR,author = {Feichtenhofer, Christoph and Pinz, Axel and Wildes, Richard P.},title = {Bags of Spacetime Energies for Dynamic Scene Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: We are dealing with the face cluster recognition problem where there are multiple images per subject in both gallery and probe sets.  It is never guaranteed to have a clear spatio-temporal relation among the multiple images of each subject. Considering that the image vectors of each subject, either in gallery or in probe, span a subspace; an algorithm, Dual Linear Regression Classification (DLRC), for the face cluster recognition problem is developed where the distance between two subspaces is defined as the similarity value between a gallery subject and a probe subject. DLRC attempts to find a "virtual" face image located in the intersection of the subspaces spanning from both clusters of face images. The "distance" between the "virtual" face images reconstructed from both subspaces is then taken as the distance between these two subspaces. We further prove that such distance can be formulated under a single linear regression model where we indeed can find the "distance" without reconstructing the "virtual" face images.  Extensive experimental evaluations demonstrated the effectiveness of DLRC algorithm compared to other algorithms.

@InProceedings{Chen_2014_CVPR,author = {Chen, Liang},title = {Dual Linear Regression Based Classification for Face Cluster Recognition},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Chuan Sun, Marshall Tappen, Hassan Foroosh

Abstract: In this paper, we propose an unsupervised framework for action spotting in videos that does not depend on any specific feature (e.g. HOG/HOF, STIP, silhouette, bag-of-words, etc.). Furthermore, our solution requires no human localization, segmentation, or framewise tracking. This is achieved by treating the problem holistically as that of extracting the internal dynamics of video cuboids by modeling them in their natural form as multilinear tensors. To extract their internal dynamics, we devised a novel Two-Phase Decomposition (TP-Decomp) of a tensor that generates very compact and discriminative representations that are robust to even heavily perturbed data. Technically, a Rank-based Tensor Core Pyramid (Rank-TCP) descriptor is generated by combining multiple tensor cores under multiple ranks, allowing to represent video cuboids in a hierarchical tensor pyramid. The problem then reduces to a template matching problem, which is solved efficiently by using two boosting strategies: (1) to reduce search space, we filter the dense trajectory cloud extracted from the target video; (2) to boost the matching speed, we perform matching in an iterative coarse-to-fine manner. Experiments on 5 benchmarks show that our method outperforms current state-of-the-art under various challenging conditions. We also created a challenging dataset called Heavily Perturbed Video Array (HPVA) to validate the robustness of our framework under heavily perturbed situations.

@InProceedings{Sun_2014_CVPR,author = {Sun, Chuan and Tappen, Marshall and Foroosh, Hassan},title = {Feature-Independent Action Spotting Without Human Localization, Segmentation or Frame-wise Tracking},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Amos Sironi, Vincent Lepetit, Pascal Fua

Abstract: We propose a robust and accurate method to extract the centerlines and scale of tubular structures in 2D images and 3D volumes.  Existing techniques rely either on filters designed to respond to ideal cylindrical structures, which lose accuracy when the linear structures become very irregular, or on classification, which is inaccurate because locations on centerlines and locations immediately next to them are extremely difficult to distinguish.  We solve this problem by reformulating centerline detection in terms of a regression problem.  We first train regressors to return the distances to the closest centerline in scale-space, and we apply them to the input images or volumes.  The centerlines and the corresponding scale then correspond to the regressors local maxima, which can be easily identified.  We show that our method outperforms state-of-the-art techniques for various 2D and 3D datasets.

@InProceedings{Sironi_2014_CVPR,author = {Sironi, Amos and Lepetit, Vincent and Fua, Pascal},title = {Multiscale Centerline Detection by Learning a Scale-Space Distance Transform},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Hyunwoo J. Kim, Nagesh Adluru, Maxwell D. Collins, Moo K. Chung, Barbara B. Bendlin, Sterling C. Johnson, Richard J. Davidson, Vikas Singh

Abstract: Linear regression is a parametric model which is ubiquitous in scientific analysis. The classical setup where the observations and responses, i.e., (xi,yi) pairs, are Euclidean is well studied. The setting where yi is manifold valued is a topic of much interest, motivated by applications in shape analysis, topic modeling, and medical imaging. Recent work gives strategies for max-margin classifiers, principal components analysis, and dictionary learning on certain types of manifolds. For parametric regression specifically, results within the last year provide mechanisms to regress one real-valued parameter, xi in R, against a manifold-valued variable, yi in M. We seek to substantially extend the operating range of such methods by deriving schemes for multivariate multiple linear regression — a manifold-valued dependent variable against multiple independent variables, i.e., f : Rn -> M. Our variational algorithm efficiently solves for multiple geodesic bases on the manifold concurrently via gradient updates. This allows us to answer questions such as: what is the relationship of the measurement at voxel y to disease when conditioned on age and gender. We show applications to statistical analysis of diffusion weighted images, which give rise to regression tasks on the manifold GL(n)/O(n) for diffusion tensor images (DTI) and the Hilbert unit sphere for orientation distribution functions (ODF) from high angular resolution acquisition. The companion open-source code is available on nitrc.org/projects/riem_mglm.

@InProceedings{Kim_2014_CVPR,author = {Kim, Hyunwoo J. and Adluru, Nagesh and Collins, Maxwell D. and Chung, Moo K. and Bendlin, Barbara B. and Johnson, Sterling C. and Davidson, Richard J. and Singh, Vikas},title = {Multivariate General Linear Models (MGLM) on Riemannian Manifolds with Applications to Statistical Analysis of Diffusion Weighted Images},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Yinghuan Shi, Heung-Il Suk, Yang Gao, Dinggang Shen

Abstract: Recently, there has been a great interest in computer-aided Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) diagnosis. Previous learning based methods defined the diagnosis process as a classification task and directly used the low-level features extracted from neuroimaging data without considering relations among them. However, from a neuroscience point of view, it's well known that a human brain is a complex system that multiple brain regions are anatomically connected and functionally inter- act with each other. Therefore, it is natural to hypothesize that the low-level features extracted from neuroimaging data are related to each other in some ways. To this end, in this paper, we first devise a coupled feature representation by utilizing intra-coupled and inter-coupled interaction relationship. Regarding multi-modal data fusion, we propose a novel coupled boosting algorithm that analyzes the pairwise coupled-diversity correlation between modalities. Specifically, we formulate a new weight updating function, which considers both incorrectly and inconsistently classified samples. In our experiments on the ADNI dataset, the proposed method presented the best performance with accuracies of 94.7% and 80.1% for AD vs. Normal Control (NC) and MCI vs. NC classifications, respectively, outperforming the competing methods and the state-of-the-art methods.

@InProceedings{Shi_2014_CVPR,author = {Shi, Yinghuan and Suk, Heung-Il and Gao, Yang and Shen, Dinggang},title = {Joint Coupled-Feature Representation and Coupled Boosting for AD Diagnosis},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Chen Chen, Junzhou Huang, Lei He, Hongsheng Li

Abstract: In this paper, we propose a novel algorithm for structured sparsity reconstruction. This algorithm is based on the iterative reweighted least squares (IRLS) framework, and accelerated by the preconditioned conjugate gradient method.  The convergence rate of the proposed algorithm is almost the same as that of the traditional IRLS algorithms, that is, exponentially fast. Moreover, with the devised preconditioner, the computational cost for each iteration is significantly less than that of traditional IRLS algorithms, which makes it feasible for large scale problems. Besides the fast convergence, this algorithm can be flexibly applied to standard sparsity, group sparsity, and overlapping group sparsity problems. Experiments are conducted on a practical application compressive sensing magnetic resonance imaging. Results demonstrate that the proposed algorithm achieves superior performance over 9 state-of-the-art algorithms in terms of both accuracy and computational cost.

@InProceedings{Chen_2014_CVPR,author = {Chen, Chen and Huang, Junzhou and He, Lei and Li, Hongsheng},title = {Preconditioning for Accelerated Iteratively Reweighted Least Squares in Structured Sparsity Reconstruction},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Luca Fiaschi, Ferran Diego, Konstantin Gregor, Martin Schiegg, Ullrich Koethe, Marta Zlatic, Fred A. Hamprecht

Abstract: We use weakly supervised structured learning to track and disambiguate the identity of multiple indistinguishable, translucent and deformable objects that can overlap for many frames. For this challenging problem, we propose a novel model which handles occlusions, complex motions and non-rigid deformations by jointly optimizing the flows of multiple latent intensities across frames. These flows are latent variables for which the user cannot directly provide labels. Instead, we leverage a structured learning formulation that uses weak user annotations to find the best hyperparameters of this model. The approach is evaluated on a challenging dataset for the tracking of multiple Drosophila larvae which we make publicly available. Our method tracks multiple larvae in spite of their poor distinguishability and minimizes the number of identity switches during prolonged mutual occlusion.

@InProceedings{Fiaschi_2014_CVPR,author = {Fiaschi, Luca and Diego, Ferran and Gregor, Konstantin and Schiegg, Martin and Koethe, Ullrich and Zlatic, Marta and Hamprecht, Fred A.},title = {Tracking Indistinguishable Translucent Objects over Time using Weakly Supervised Structured Learning},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Demian Wassermann, James Ross, George Washko, William M. Wells III, Raul San Jose-Estepar

Abstract: The main contribution of this work is a framework to register anatomical structures characterized as a point set where each point has an associated symmetric matrix. These matrices can represent problem-dependent characteristics of the registered structure. For example, in airways, matrices can represent the orientation and thickness of the structure. Our framework relies on a dense tensor field representation which we implement sparsely as a kernel mixture of tensor fields. We equip the space of tensor fields with a norm that serves as a similarity measure. To calculate the optimal transformation between two structures we minimize this measure using an analytical gradient for the similarity measure and the deformation field, which we restrict to be a diffeomorphism. We illustrate the value of our tensor field model by comparing our results with scalar and vector field based models. Finally, we evaluate our registration algorithm on synthetic data sets and validate our approach on manually annotated airway trees.

@InProceedings{Wassermann_2014_CVPR,author = {Wassermann, Demian and Ross, James and Washko, George and Wells, III, William M. and San Jose-Estepar, Raul},title = {Deformable Registration of Feature-Endowed Point Sets Based on Tensor Fields},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: In this study, we propose the application of principal components analysis (PCA) to scale-spaces. PCA is a standard method used in computer vision. The translation of an input image into scale-space is a continuous operation, which requires the extension of conventional finite matrix-based PCA to an infinite number of dimensions. In this study, we use spectral decomposition to resolve this infinite eigenproblem by integration and we propose an approximate solution based on polynomial equations. To clarify its eigensolutions, we apply spectral decomposition to the Gaussian scale-space and scale-normalized Laplacian of Gaussian (LoG) space. As an application of this proposed method, we introduce a method for generating Gaussian blur images and scale-normalized LoG images, where we demonstrate that the accuracy of these images can be very high when calculating an arbitrary scale using a simple linear combination. We also propose a new Scale Invariant Feature Transform (SIFT) detector as a more practical example.

@InProceedings{Koutaki_2014_CVPR,author = {Koutaki, Gou and Uchimura, Keiichi},title = {Scale-space Processing Using Polynomial Representations},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}


Abstract: This paper addresses extracting two layers from an image where one layer is smoother than the other.  This problem arises most notably in intrinsic image decomposition and reflection interference removal.  Layer decomposition from a single-image is inherently ill-posed and solutions require additional constraints to be enforced.  We introduce a novel strategy that regularizes the gradients of the two layers such that one has a long tail distribution and the other a short tail distribution.  While imposing the long tail distribution is a common practice, our introduction of the short tail distribution on the second layer is unique.  We formulate our problem in a probabilistic framework and describe an optimization scheme to solve this regularization with only a few iterations.  We apply our approach to the intrinsic image and reflection removal problems and demonstrate high quality layer separation on par with other techniques but being significantly faster than prevailing methods.

@InProceedings{Li_2014_CVPR,author = {Li, Yu and Brown, Michael S.},title = {Single Image Layer Separation using Relative Smoothness},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}