mtybilly/Instruct2DS huggingface dataset:
In Defense of Sparsity Based Face Recognition
Authors: Jiani Hu, Jun Guo Weihong Deng
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: The success of sparse representation based classification (SRC) has largely boosted the research of sparsity based face recognition in recent years. A prevailing view is that the sparsity based face recognition performs well only when the training images have been carefully controlled and the number of samples per class is sufficiently large. This paper challenges the prevailing view by proposing a "prototype plus variation" representation model for sparsity based face recognition. Based on the new model, a Superposed SRC (SSRC), in which the dictionary is assembled by the class centroids and the sample-to-centroid differences, leads to a substantial improvement on SRC. The experiments results on AR, FERET and FRGC databases validate that, if the proposed prototype plus variation representation model is applied, sparse coding plays a crucial role in face recognition, and performs well even when the dictionary bases are collected under uncontrolled conditions and only a single sample per classes is available.
Image Matting with Local and Nonlocal Smooth Priors
Authors: Dongqing Zou, Steven Zhiying Zhou, Qinping Zhao, Ping Tan Xiaowu Chen
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: In this paper we propose a novel alpha matting method with local and nonlocal smooth priors. We observe that the manifold preserving editing propagation [4] essentially introduced a nonlocal smooth prior on the alpha matte. This nonlocal smooth prior and the well known local smooth prior from matting Laplacian complement each other. So we combine them with a simple data term from color sampling in a graph model for nature image matting. Our method has a closed-form solution and can be solved efficiently. Compared with the state-of-the-art methods, our method produces more accurate results according to the evaluation on standard benchmark datasets.
Image Tag Completion via Image-Specific and Tag-Specific Linear Sparse Reconstructions
Authors: Guiguang Ding, Mingqing Hu, Jianmin Wang, Xiaojun Ye Zijia Lin
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Though widely utilized for facilitating image management, user-provided image tags are usually incomplete and insufficient to describe the whole semantic content of corresponding images, resulting in performance degradations in tag-dependent applications and thus necessitating effective tag completion methods. In this paper, we propose a novel scheme denoted as LSR for automatic image tag completion via image-specific and tag-specific Linear Sparse Reconstructions. Given an incomplete initial tagging matrix with each row representing an image and each column representing a tag, LSR optimally reconstructs each image (i.e. row) and each tag (i.e. column) with remaining ones under constraints of sparsity, considering imageimage similarity, image-tag association and tag-tag concurrence. Then both image-specific and tag-specific reconstruction values are normalized and merged for selecting missing related tags. Extensive experiments conducted on both benchmark dataset and web images well demonstrate the effectiveness of the proposed LSR.
Non-parametric Filtering for Geometric Detail Extraction and Material Representation
Authors: Jason Rock, Yang Wang, David Forsyth Zicheng Liao
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Geometric detail is a universal phenomenon in real world objects. It is an important component in object modeling, but not accounted for in current intrinsic image works. In this work, we explore using a non-parametric method to separate geometric detail from intrinsic image components. We further decompose an image as albedo * (coarse-scale shading + shading detail). Our decomposition offers quantitative improvement in albedo recovery and material classification.Our method also enables interesting image editing activities, including bump removal, geometric detail smoothing/enhancement and material transfer.
Bottom-Up Segmentation for Top-Down Detection
Authors: Roozbeh Mottaghi, Alan Yuille, Raquel Urtasun Sanja Fidler
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: In this paper we are interested in how semantic segmentation can help object detection. Towards this goal, we propose a novel deformable part-based model which exploits region-based segmentation algorithms that compute candidate object regions by bottom-up clustering followed by ranking of those regions. Our approach allows every detection hypothesis to select a segment (including void), and scores each box in the image using both the traditional HOG filters as well as a set of novel segmentation features. Thus our model "blends" between the detector and segmentation models. Since our features can be computed very efficiently given the segments, we maintain the same complexity as the original DPM [14]. We demonstrate the effectiveness of our approach in PASCAL VOC 2010, and show that when employing only a root filter our approach outperforms Dalal & Triggs detector [12] on all classes, achieving 13% higher average AP. When employing the parts, we outperform the original DPM [14] in 19 out of 20 classes, achieving an improvement of 8% AP. Furthermore, we outperform the previous state-of-the-art on VOC'10 test by 4%.
Expanded Parts Model for Human Attribute and Action Recognition in Still Images
Authors: Frederic Jurie, Cordelia Schmid Gaurav Sharma
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We propose a new model for recognizing human attributes (e.g. wearing a suit, sitting, short hair) and actions (e.g. running, riding a horse) in still images. The proposed model relies on a collection of part templates which are learnt discriminatively to explain specific scale-space locations in the images (in human centric coordinates). It avoids the limitations of highly structured models, which consist of a few (i.e. a mixture of) 'average' templates. To learn our model, we propose an algorithm which automatically mines out parts and learns corresponding discriminative templates with their respective locations from a large number of candidate parts. We validate the method on recent challenging datasets: (i) Willow 7 actions [7], (ii) 27 Human Attributes (HAT) [25], and (iii) Stanford 40 actions [37]. We obtain convincing qualitative and state-of-the-art quantitative results on the three datasets.
Inductive Hashing on Manifolds
Authors: Chunhua Shen, Qinfeng Shi, Anton van den Hengel, Zhenmin Tang Fumin Shen
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Learning based hashing methods have attracted considerable attention due to their ability to greatly increase the scale at which existing algorithms may operate. Most of these methods are designed to generate binary codes that preserve the Euclidean distance in the original space. Manifold learning techniques, in contrast, are better able to model the intrinsic structure embedded in the original highdimensional data. The complexity of these models, and the problems with out-of-sample data, have previously rendered them unsuitable for application to large-scale embedding, however. In this work, we consider how to learn compact binary embeddings on their intrinsic manifolds. In order to address the above-mentioned difficulties, we describe an efficient, inductive solution to the out-of-sample data problem, and a process by which non-parametric manifold learning may be used as the basis of a hashing method. Our proposed approach thus allows the development of a range of new hashing techniques exploiting the flexibility of the wide variety of manifold learning approaches available. We particularly show that hashing on the basis of t-SNE [29] outperforms state-of-the-art hashing methods on large-scale benchmark datasets, and is very effective for image classification with very short code lengths.
Robust Feature Matching with Alternate Hough and Inverted Hough Transforms
Authors: Yen-Yu Lin, Bing-Yu Chen Hsin-Yi Chen
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We present an algorithm that carries out alternate Hough transform and inverted Hough transform to establish feature correspondences, and enhances the quality of matching in both precision and recall. Inspired by the fact that nearby features on the same object share coherent homographies in matching, we cast the task of feature matching as a density estimation problem in the Hough space spanned by the hypotheses of homographies. Specifically, we project all the correspondences into the Hough space, and determine the correctness of the correspondences by their respective densities. In this way, mutual verification of relevant correspondences is activated, and the precision of matching is boosted. On the other hand, we infer the concerted homographies propagated from the locally grouped features, and enrich the correspondence candidates for each feature. The recall is hence increased. The two processes are tightly coupled. Through iterative optimization, plausible enrichments are gradually revealed while more correct correspondences are detected. Promising experimental results on three benchmark datasets manifest the effectiveness of the proposed approach.
Fast Object Detection with Entropy-Driven Evaluation
Authors: Carlos Becker, Francois Fleuret, Pascal Fua Raphael Sznitman
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Cascade-style approaches to implementing ensemble classifiers can deliver significant speed-ups at test time. While highly effective, they remain challenging to tune and their overall performance depends on the availability of large validation sets to estimate rejection thresholds. These characteristics are often prohibitive and thus limit their applicability. We introduce an alternative approach to speeding-up classifier evaluation which overcomes these limitations. It involves maintaining a probability estimate of the class label at each intermediary response and stopping when the corresponding uncertainty becomes small enough. As a result, the evaluation terminates early based on the sequence of responses observed. Furthermore, it does so independently of the type of ensemble classifier used or the way it was trained. We show through extensive experimentation that our method provides 2 to 10 fold speed-ups, over existing state-of-the-art methods, at almost no loss in accuracy on a number of object classification tasks.
Event Recognition in Videos by Learning from Heterogeneous Web Sources
Authors: Lixin Duan, Dong Xu Lin Chen
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: In this work, we propose to leverage a large number of loosely labeled web videos (e.g., from YouTube) and web images (e.g., from Google/Bing image search) for visual event recognition in consumer videos without requiring any labeled consumer videos. We formulate this task as a new multi-domain adaptation problem with heterogeneous sources, in which the samples from different source domains can be represented by different types of features with different dimensions (e.g., the SIFT features from web images and space-time (ST) features from web videos) while the target domain samples have all types of features. To effectively cope with the heterogeneous sources where some source domains are more relevant to the target domain, we propose a new method called Multi-domain Adaptation with Heterogeneous Sources (MDA-HS) to learn an optimal target classifier, in which we simultaneously seek the optimal weights for different source domains with different types of features as well as infer the labels of unlabeled target domain data based on multiple types of features. We solve our optimization problem by using the cutting-plane algorithm based on group-based multiple kernel learning. Comprehensive experiments on two datasets demonstrate the effectiveness of MDA-HS for event recognition in consumer videos.
Discriminative Color Descriptors
Authors: Joost van de Weijer, Fahad Shahbaz Khan, Damien Muselet, Christophe Ducottet, Cecile Barat Rahat Khan
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Color description is a challenging task because of large variations in RGB values which occur due to scene accidental events, such as shadows, shading, specularities, illuminant color changes, and changes in viewing geometry. Traditionally, this challenge has been addressed by capturing the variations in physics-based models, and deriving invariants for the undesired variations. The drawback of this approach is that sets of distinguishable colors in the original color space are mapped to the same value in the photometric invariant space. This results in a drop of discriminative power of the color description. In this paper we take an information theoretic approach to color description. We cluster color values together based on their discriminative power in a classification problem. The clustering has the explicit objective to minimize the drop of mutual information of the final representation. We show that such a color description automatically learns a certain degree of photometric invariance. We also show that a universal color representation, which is based on other data sets than the one at hand, can obtain competing performance. Experiments show that the proposed descriptor outperforms existing photometric invariants. Furthermore, we show that combined with shape description these color descriptors obtain excellent results on four challenging datasets, namely, PASCAL VOC 2007, Flowers-102, Stanford dogs-120 and Birds-200.
Optical Flow Estimation Using Laplacian Mesh Energy
Authors: Darren Cosker, Matthew Brown, Rui Tang Wenbin Li
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: In this paper we present a novel non-rigid optical flow algorithm for dense image correspondence and non-rigid registration. The algorithm uses a unique Laplacian Mesh Energy term to encourage local smoothness whilst simultaneously preserving non-rigid deformation. Laplacian deformation approaches have become popular in graphics research as they enable mesh deformations to preserve local surface shape. In this work we propose a novel Laplacian Mesh Energy formula to ensure such sensible local deformations between image pairs. We express this wholly within the optical flow optimization, and show its application in a novel coarse-to-fine pyramidal approach. Our algorithm achieves the state-of-the-art performance in all trials on the Garg et al. dataset, and top tier performance on the Middlebury evaluation.
Constrained Clustering and Its Application to Face Clustering in Videos
Authors: Yifan Zhang, Bao-Gang Hu, Qiang Ji Baoyuan Wu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: In this paper, we focus on face clustering in videos. Given the detected faces from real-world videos, we partition all faces into K disjoint clusters. Different from clustering on a collection of facial images, the faces from videos are organized as face tracks and the frame index of each face is also provided. As a result, many pairwise constraints between faces can be easily obtained from the temporal and spatial knowledge of the face tracks. These constraints can be effectively incorporated into a generative clustering model based on the Hidden Markov Random Fields (HMRFs). Within the HMRF model, the pairwise constraints are augmented by label-level and constraint-level local smoothness to guide the clustering process. The parameters for both the unary and the pairwise potential functions are learned by the simulated field algorithm, and the weights of constraints can be easily adjusted. We further introduce an efficient clustering framework specially for face clustering in videos, considering that faces in adjacent frames of the same face track are very similar. The framework is applicable to other clustering algorithms to significantly reduce the computational cost. Experiments on two face data sets from real-world videos demonstrate the significantly improved performance of our algorithm over state-of-theart algorithms.
Subcategory-Aware Object Classification
Authors: Wei Xia, Qiang Chen, Jianshi Feng, Zhongyang Huang, Shuicheng Yan Jian Dong
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: In this paper, we introduce a subcategory-aware object classification framework to boost category level object classification performance. Motivated by the observation of considerable intra-class diversities and inter-class ambiguities in many current object classification datasets, we explicitly split data into subcategories by ambiguity guided subcategory mining. We then train an individual model for each subcategory rather than attempt to represent an object category with a monolithic model. More specifically, we build the instance affinity graph by combining both intraclass similarity and inter-class ambiguity. Visual subcategories, which correspond to the dense subgraphs, are detected by the graph shift algorithm and seamlessly integrated into the state-of-the-art detection assisted classification framework. Finally the responses from subcategory models are aggregated by subcategory-aware kernel regression. The extensive experiments over the PASCAL VOC 2007 and PASCAL VOC 2010 databases show the state-ofthe-art performance from our framework.
Face Recognition in Movie Trailers via Mean Sequence Sparse Representation-Based Classification
Authors: Alan Wright, Mubarak Shah Enrique G. Ortiz
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: This paper presents an end-to-end video face recognition system, addressing the difficult problem of identifying a video face track using a large dictionary of still face images of a few hundred people, while rejecting unknown individuals. A straightforward application of the popular n-minimization for face recognition on a frame-by-frame basis is prohibitively expensive, so we propose a novel algorithm Mean Sequence SRC (MSSRC) that performs video face recognition using a joint optimization leveraging all of the available video data and the knowledge that the face track frames belong to the same individual. By adding a strict temporal constraint to the ii-minimization that forces individual frames in a face track to all reconstruct a single identity, we show the optimization reduces to a single minimization over the mean of the face track. We also introduce a new Movie Trailer Face Dataset collected from 101 movie trailers on YouTube. Finally, we show that our method matches or outperforms the state-of-the-art on three existing datasets (YouTube Celebrities, YouTube Faces, and Buffy) and our unconstrained Movie Trailer Face Dataset. More importantly, our method excels at rejecting unknown identities by at least 8% in average precision.
Multi-attribute Queries: To Merge or Not to Merge?
Authors: Ali Diba, Devi Parikh, Ali Farhadi Mohammad Rastegari
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Users often have very specific visual content in mind that they are searching for. The most natural way to communicate this content to an image search engine is to use keywords that specify various properties or attributes of the content. A naive way of dealing with such multi-attribute queries is the following: train a classifier for each attribute independently, and then combine their scores on images to judge their fit to the query. We argue that this may not be the most effective or efficient approach. Conjunctions of attribute often correspond to very characteristic appearances. It would thus be beneficial to train classifiers that detect these conjunctions as a whole. But not all conjunctions result in such tight appearance clusters. So given a multi-attribute query, which conjunctions should we model? An exhaustive evaluation of all possible conjunctions would be time consuming. Hence we propose an optimization approach that identifies beneficial conjunctions without explicitly training the corresponding classifier. It reasons about geometric quantities that capture notions similar to intraand inter-class variances. We exploit a discriminative binary space to compute these geometric quantities efficiently. Experimental results on two challenging datasets of objects and birds show that our proposed approach can improve performance significantly over several strong baselines, while being an order of magnitude faster than exhaustively searching through all possible conjunctions.
Towards Efficient and Exact MAP-Inference for Large Scale Discrete Computer Vision Problems via Combinatorial Optimization
Authors: Markus Speth, Gerhard Reinelt, Christoph Schnorr Jorg Hendrik Kappes
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Discrete graphical models (also known as discrete Markov random fields) are a major conceptual tool to model the structure of optimization problems in computer vision. While in the last decade research has focused on fast approximative methods, algorithms that provide globally optimal solutions have come more into the research focus in the last years. However, large scale computer vision problems seemed to be out of reach for such methods. In this paper we introduce a promising way to bridge this gap based on partial optimality and structural properties of the underlying problem factorization. Combining these preprocessing steps, we are able to solve grids of size 2048 x 2048 in less than 90 seconds. On the hitherto unsolvable Chinese character dataset of Nowozin et al. we obtain provably optimal results in 56% of the instances and achieve competitive runtimes on other recent benchmark problems. While in the present work only generalized Potts models are considered, an extension to general graphical models seems to be feasible.
Plane-Based Content Preserving Warps for Video Stabilization
Authors: Hailin Jin, Yi Ma Zihan Zhou
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Recently, a new image deformation technique called content-preserving warping (CPW) has been successfully employed to produce the state-of-the-art video stabilization results in many challenging cases. The key insight of CPW is that the true image deformation due to viewpoint change can be well approximated by a carefully constructed warp using a set of sparsely constructed 3D points only. However, since CPW solely relies on the tracked feature points to guide the warping, it works poorly in large textureless regions, such as ground and building interiors. To overcome this limitation, in this paper we present a hybrid approach for novel view synthesis, observing that the textureless regions often correspond to large planar surfaces in the scene. Particularly, given a jittery video, we first segment each frame into piecewise planar regions as well as regions labeled as non-planar using Markov random fields. Then, a new warp is computed by estimating a single homography for regions belong to the same plane, while inheriting results from CPW in the non-planar regions. We demonstrate how the segmentation information can be efficiently obtained and seamlessly integrated into the stabilization framework. Experimental results on a variety of real video sequences verify the effectiveness of our method.
Three-Dimensional Bilateral Symmetry Plane Estimation in the Phase Domain
Authors: Prabhu Kaliamoorthi, Vittal Premachandran Ramakrishna Kakarala
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We show that bilateral symmetry plane estimation for three-dimensional (3-D) shapes may be carried out accurately, and efficiently, in the spherical harmonic domain. Our methods are valuable for applications where spherical harmonic expansion is already employed, such as 3-D shape registration, morphometry, and retrieval. We show that the presence of bilateral symmetry in the 3-D shape is equivalent to a linear phase structure in the corresponding spherical harmonic coefficients, and provide algorithms for estimating the orientation of the symmetry plane. The benefit of using spherical harmonic phase is that symmetry estimation reduces to matching a compact set of descriptors, without the need to solve a correspondence problem. Our methods work on point clouds as well as large-scale mesh models of 3-D shapes.
Watching Unlabeled Video Helps Learn New Human Actions from Very Few Labeled Snapshots
Authors: Kristen Grauman Chao-Yeh Chen
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: We propose an approach to learn action categories from static images that leverages prior observations of generic human motion to augment its training process. Using unlabeled video containing various human activities, the system first learns how body pose tends to change locally in time. Then, given a small number of labeled static images, it uses that model to extrapolate beyond the given exemplars and generate "synthetic" training examples--poses that could link the observed images and/or immediately precede or follow them in time. In this way, we expand the training set without requiring additional manually labeled examples. We explore both example-based and manifold-based methods to implement our idea. Applying our approach to recognize actions in both images and video, we show it enhances a state-of-the-art technique when very few labeled training examples are available.
Long-Term Occupancy Analysis Using Graph-Based Optimisation in Thermal Imagery
Authors: Anders Jorgensen, Thomas B. Moeslund Rikke Gade
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: This paper presents a robust occupancy analysis system for thermal imaging. Reliable detection of people is very hard in crowded scenes, due to occlusions and segmentation problems. We therefore propose a framework that optimises the occupancy analysis over long periods by including information on the transition in occupancy, when people enter or leave the monitored area. In stable periods, with no activity close to the borders, people are detected and counted which contributes to a weighted histogram. When activity close to the border is detected, local tracking is applied in order to identify a crossing. After a full sequence, the number of people during all periods are estimated using a probabilistic graph search optimisation. The system is tested on a total of 51,000 frames, captured in sports arenas. The mean error for a 30-minute period containing 3-13 people is 4.44 %, which is a half of the error percentage optained by detection only, and better than the results of comparable work. The framework is also tested on a public available dataset from an outdoor scene, which proves the generality of the method.
Topical Video Object Discovery from Key Frames by Modeling Word Co-occurrence Prior
Authors: Junsong Yuan, Gang Hua Gangqiang Zhao
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: A topical video object refers to an object that is frequently highlighted in a video. It could be, e.g., the product logo and the leading actor/actress in a TV commercial. We propose a topic model that incorporates a word co-occurrence prior for efficient discovery of topical video objects from a set of key frames. Previous work using topic models, such as Latent Dirichelet Allocation (LDA), for video object discovery often takes a bag-of-visual-words representation, which ignored important co-occurrence information among the local features. We show that such data driven co-occurrence information from bottom-up can conveniently be incorporated in LDA with a Gaussian Markov prior, which combines top down probabilistic topic modeling with bottom up priors in a unified model. Our experiments on challenging videos demonstrate that the proposed approach can discover different types of topical objects despite variations in scale, view-point, color and lighting changes, or even partial occlusions. The efficacy of the co-occurrence prior is clearly demonstrated when comparing with topic models without such priors.
Keypoints from Symmetries by Wave Propagation
Authors: Alessandro Lanza, Luigi Di Stefano Samuele Salti
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: The paper conjectures and demonstrates that repeatable keypoints based on salient symmetries at different scales can be detected by a novel analysis grounded on the wave equation rather than the heat equation underlying traditional Gaussian scale-space theory. While the image structures found by most state-of-the-art detectors, such as blobs and corners, occur typically on planar highly textured surfaces, salient symmetries are widespread in diverse kinds of images, including those related to untextured objects, which are hardly dealt with by current feature-based recognition pipelines. We provide experimental results on standard datasets and also contribute with a new dataset focused on untextured objects. Based on the positive experimental results, we hope to foster further research on the promising topic of scale invariant analysis through the wave equation.
Robust Real-Time Tracking of Multiple Objects by Volumetric Mass Densities
Authors: Sabine Sternig, Thomas Mauthner, Peter M. Roth, Horst Bischof Horst Possegger
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Combining foreground images from multiple views by projecting them onto a common ground-plane has been recently applied within many multi-object tracking approaches. These planar projections introduce severe artifacts and constrain most approaches to objects moving on a common 2D ground-plane. To overcome these limitations, we introduce the concept of an occupancy volume exploiting the full geometry and the objects' center of mass and develop an efficient algorithm for 3D object tracking. Individual objects are tracked using the local mass density scores within a particle filter based approach, constrained by a Voronoi partitioning between nearby trackers. Our method benefits from the geometric knowledge given by the occupancy volume to robustly extract features and train classifiers on-demand, when volumetric information becomes unreliable. We evaluate our approach on several challenging real-world scenarios including the public APIDIS dataset. Experimental evaluations demonstrate significant improvements compared to state-of-theart methods, while achieving real-time performance.
A Divide-and-Conquer Method for Scalable Low-Rank Latent Matrix Pursuit
Authors: Hanjiang Lai, Cong Liu, Shuicheng Yan Yan Pan
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Data fusion, which effectively fuses multiple prediction lists from different kinds of features to obtain an accurate model, is a crucial component in various computer vision applications. Robust late fusion (RLF) is a recent proposed method that fuses multiple output score lists from different models via pursuing a shared low-rank latent matrix. Despite showing promising performance, the repeated full Singular Value Decomposition operations in RLF's optimization algorithm limits its scalability in real world vision datasets which usually have large number of test examples. To address this issue, we provide a scalable solution for large-scale low-rank latent matrix pursuit by a divide-andconquer method. The proposed method divides the original low-rank latent matrix learning problem into two sizereduced subproblems, which may be solved via any base algorithm, and combines the results from the subproblems to obtain the final solution. Our theoretical analysis shows that with fixed probability, the proposed divide-and-conquer method has recovery guarantees comparable to those of its base algorithm. Moreover, we develop an efficient base algorithm for the corresponding subproblems by factorizing a large matrix into the product of two size-reduced matrices. We also provide high probability recovery guarantees of the base algorithm. The proposed method is evaluated on various fusion problems in object categorization and video event detection. Under comparable accuracy, the proposed method performs more than 180 times faster than the stateof-the-art baselines on the CCV dataset with about 4,500 test examples for video event detection.
PDM-ENLOR: Learning Ensemble of Local PDM-Based Regressions
Authors: Uday Kurkure, Ioannis A. Kakadiaris Yen H. Le
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Statistical shape models, such as Active Shape Models (ASMs), suffer from their inability to represent a large range of variations of a complex shape and to account for the large errors in detection of model points. We propose a novel method (dubbed PDM-ENLOR) that overcomes these limitations by locating each shape model point individually using an ensemble of local regression models and appearance cues from selected model points. Our method first detects a set of reference points which were selected based on their saliency during training. For each model point, an ensemble of regressors is built. From the locations of the detected reference points, each regressor infers a candidate location for that model point using local geometric constraints, encoded by a point distribution model (PDM). The final location of that point is determined as a weighted linear combination, whose coefficients are learnt from the training data, of candidates proposed from its ensemble's component regressors. We use different subsets of reference points as explanatory variables for the component regressors to provide varying degrees of locality for the models in each ensemble. This helps our ensemble model to capture a larger range of shape variations as compared to a single PDM. We demonstrate the advantages of our method on the challenging problem of segmenting gene expression images of mouse brain.
Beta Process Joint Dictionary Learning for Coupled Feature Spaces with Application to Single Image Super-Resolution
Authors: Hairong Qi, Russell Zaretzki Li He
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: This paper addresses the problem of learning overcomplete dictionaries for the coupled feature spaces, where the learned dictionaries also reflect the relationship between the two spaces. A Bayesian method using a beta process prior is applied to learn the over-complete dictionaries. Compared to previous couple feature spaces dictionary learning algorithms, our algorithm not only provides dictionaries that customized to each feature space, but also adds more consistent and accurate mapping between the two feature spaces. This is due to the unique property of the beta process model that the sparse representation can be decomposed to values and dictionary atom indicators. The proposed algorithm is able to learn sparse representations that correspond to the same dictionary atoms with the same sparsity but different values in coupled feature spaces, thus bringing consistent and accurate mapping between coupled feature spaces. Another advantage of the proposed method is that the number of dictionary atoms and their relative importance may be inferred non-parametrically. We compare the proposed approach to several state-of-the-art dictionary learning methods by applying this method to single image super-resolution. The experimental results show that dictionaries learned by our method produces the best superresolution results compared to other state-of-the-art methods.
Fast Trust Region for Segmentation
Authors: Frank R. Schmidt, Yuri Boykov Lena Gorelick
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Trust region is a well-known general iterative approach to optimization which offers many advantages over standard gradient descent techniques. In particular, it allows more accurate nonlinear approximation models. In each iteration this approach computes a global optimum of a suitable approximation model within a fixed radius around the current solution, a.k.a. trust region. In general, this approach can be used only when some efficient constrained optimization algorithm is available for the selected nonlinear (more accurate) approximation model. In this paper we propose a Fast Trust Region (FTR) approach for optimization of segmentation energies with nonlinear regional terms, which are known to be challenging for existing algorithms. These energies include, but are not limited to, KL divergence and Bhattacharyya distance between the observed and the target appearance distributions, volume constraint on segment size, and shape prior constraint in a form of L 2 distance from target shape moments. Our method is 1-2 orders of magnitude faster than the existing state-of-the-art methods while converging to comparable or better solutions.
Area Preserving Brain Mapping
Authors: Wei Zeng, Rui Shi, Yalin Wang, Jian Sun, Xianfeng Gu Zhengyu Su
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Brain mapping transforms the brain cortical surface to canonical planar domains, which plays a fundamental role in morphological study. Most existing brain mapping methods are based on angle preserving maps, which may introduce large area distortions. This work proposes an area preserving brain mapping method based on MongeBrenier theory. The brain mapping is intrinsic to the Riemannian metric, unique, and diffeomorphic. The computation is equivalent to convex energy minimization and power Voronoi diagram construction. Comparing to the existing approaches based on Monge-Kantorovich theory, the proposed one greatly reduces the complexity (from n ,tunknowns to n ), and improves the simplicity and efficiency. Experimental results on caudate nucleus surface mapping and cortical surface mapping demonstrate the efficacy and efficiency of the proposed method. Conventional methods for caudate nucleus surface mapping may suffer from numerical instability; in contrast, current method produces diffeomorpic mappings stably. In the study of cortical surface classification for recognition of Alzheimer's Disease, the proposed method outperforms some other morphometry features.
Multi-level Discriminative Dictionary Learning towards Hierarchical Visual Categorization
Authors: Shuhui Wang, Gang Sun, Shuqiang Jiang, Qingming Huang Li Shen
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: For the task of visual categorization, the learning model is expected to be endowed with discriminative visual feature representation and flexibilities in processing many categories. Many existing approaches are designed based on a flat category structure, or rely on a set of pre-computed visual features, hence may not be appreciated for dealing with large numbers of categories. In this paper, we propose a novel dictionary learning method by taking advantage of hierarchical category correlation. For each internode of the hierarchical category structure, a discriminative dictionary and a set of classification models are learnt for visual categorization, and the dictionaries in different layers are learnt to exploit the discriminative visual properties of different granularity. Moreover, the dictionaries in lower levels also inherit the dictionary of ancestor nodes, so that categories in lower levels are described with multi-scale visual information using our dictionary learning approach. Experiments on ImageNet object data subset and SUN397 scene dataset demonstrate that our approach achieves promising performance on data with large numbers of classes compared with some state-of-the-art methods, and is more efficient in processing large numbers of categories.
BFO Meets HOG: Feature Extraction Based on Histograms of Oriented p.d.f. Gradients for Image Classification
Authors: Takumi Kobayashi
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Image classification methods have been significantly developed in the last decade. Most methods stem from bagof-features (BoF) approach and it is recently extended to a vector aggregation model, such as using Fisher kernels. In this paper, we propose a novel feature extraction method for image classification. Following the BoF approach, a plenty of local descriptors are first extracted in an image and the proposed method is built upon the probability density function (p.d.f) formed by those descriptors. Since the p.d.f essentially represents the image, we extract the features from the p.d.f by means of the gradients on the p.d.f. The gradients, especially their orientations, effectively characterize the shape of the p.d.f from the geometrical viewpoint. We construct the features by the histogram of the oriented p.d.f gradients via orientation coding followed by aggregation of the orientation codes. The proposed image features, imposing no specific assumption on the targets, are so general as to be applicable to any kinds of tasks regarding image classifications. In the experiments on object recognition and scene classification using various datasets, the proposed method exhibits superior performances compared to the other existing methods.
Single-Sample Face Recognition with Image Corruption and Misalignment via Sparse Illumination Transfer
Authors: Allen Y. Yang, Zihan Zhou, S. Shankar Sastry, Yi Ma Liansheng Zhuang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Single-sample face recognition is one of the most challenging problems in face recognition. We propose a novel face recognition algorithm to address this problem based on a sparse representation based classification (SRC) framework. The new algorithm is robust to image misalignment and pixel corruption, and is able to reduce required training images to one sample per class. To compensate the missing illumination information typically provided by multiple training images, a sparse illumination transfer (SIT) technique is introduced. The SIT algorithms seek additional illumination examples of face images from one or more additional subject classes, and form an illumination dictionary. By enforcing a sparse representation of the query image, the method can recover and transfer the pose and illumination information from the alignment stage to the recognition stage. Our extensive experiments have demonstrated that the new algorithms significantly outperform the existing algorithms in the single-sample regime and with less restrictions. In particular, the face alignment accuracy is comparable to that of the well-known Deformable SRC algorithm using multiple training images; and the face recognition accuracy exceeds those of the SRC and Extended SRC algorithms using hand labeled alignment initialization.
GeoF: Geodesic Forests for Learning Coupled Predictors
Authors: Pushmeet Kohli, Jamie Shotton, Antonio Criminisi Peter Kontschieder
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: Conventional decision forest based methods for image labelling tasks like object segmentation make predictions for each variable (pixel) independently [3, 5, 8]. This prevents them from enforcing dependencies between variables and translates into locally inconsistent pixel labellings. Random field models, instead, encourage spatial consistency of labels at increased computational expense. This paper presents a new and efficient forest based model that achieves spatially consistent semantic image segmentation by encoding variable dependencies directly in the feature space the forests operate on. Such correlations are captured via new long-range, soft connectivity features, computed via generalized geodesic distance transforms. Our model can be thought of as a generalization of the successful Semantic Texton Forest, Auto-Context, and Entangled Forest models. A second contribution is to show the connection between the typical Conditional Random Field (CRF) energy and the forest training objective. This analysis yields a new objective for training decision forests that encourages more accurate structured prediction. Our GeoF model is validated quantitatively on the task of semantic image segmentation, on four challenging and very diverse image datasets. GeoF outperforms both stateof-the-art forest models and the conventional pairwise CRF.
Improving Image Matting Using Comprehensive Sampling Sets
Authors: Deepu Rajan, Brian Price, Scott Cohen Ehsan Shahrian
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013 (Main Conference Track), 2013

Abstract: In this paper, we present a new image matting algorithm that achieves state-of-the-art performance on a benchmark dataset of images. This is achieved by solving two major problems encountered by current sampling based algorithms. The first is that the range in which the foreground and background are sampled is often limited to such an extent that the true foreground and background colors are not present. Here, we describe a method by which a more comprehensive and representative set of samples is collected so as not to miss out on the true samples. This is accomplished by expanding the sampling range for pixels farther from the foreground or background boundary and ensuring that samples from each color distribution are included. The second problem is the overlap in color distributions of foreground and background regions. This causes sampling based methods to fail to pick the correct samples for foreground and background. Our design of an objective function forces those foreground and background samples to be picked that are generated from well-separated distributions. Comparison on the dataset at and evaluation by www.alphamatting.com shows that the proposed method ranks first in terms of error measures used in the website.