pwc-archive/papers-with-abstracts huggingface dataset:
Image Matting with Local and Nonlocal Smooth Priors
Authors: Xiaowu Chen, Dongqing Zou, Steven Zhiying Zhou, Qinping Zhao, Ping Tan
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Image Matting

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: Zijia Lin, Guiguang Ding, Mingqing Hu, Jian-Min Wang, Xiaojun Ye
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Management, TAG

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: Zicheng Liao, Jason Rock, Yang Wang, David Forsyth
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6

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: Sanja Fidler, Roozbeh Mottaghi, Alan Yuille, Raquel Urtasun
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Clustering, object-detection, Object Detection, Segmentation, Semantic Segmentation

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: Gaurav Sharma, Frederic Jurie, Cordelia Schmid
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Action Recognition, Action Recognition In Still Images, Attribute, Temporal Action Localization

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.
Robust Feature Matching with Alternate Hough and Inverted Hough Transforms
Authors: Hsin-Yi Chen, Yen-Yu Lin, Bing-Yu Chen
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Density Estimation

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: Raphael Sznitman, Carlos Becker, Francois Fleuret, Pascal Fua
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Object, object-detection, Object Detection, Real-Time Object Detection

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: Lin Chen, Lixin Duan, Dong Xu
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Domain Adaptation, Image Retrieval

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: Rahat Khan, Joost Van de Weijer, Fahad Shahbaz Khan, Damien Muselet, Christophe Ducottet, Cecile Barat
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Clustering

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: Wenbin Li, Darren Cosker, Matthew Brown, Rui Tang
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Optical Flow Estimation

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: Baoyuan Wu, Yifan Zhang, Bao-Gang Hu, Qiang Ji
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Clustering, Constrained Clustering, Face Clustering

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: Jian Dong, Wei Xia, Qiang Chen, Jianshi Feng, Zhongyang Huang, Shuicheng Yan
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Classification, General Classification, Object

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: Enrique. G. Ortiz, Alan Wright, Mubarak Shah
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Face Recognition, General Classification, Sparse Representation-based Classification

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: Mohammad Rastegari, Ali Diba, Devi Parikh, Ali Farhadi
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Attribute, Image Retrieval

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: Jorg Hendrik Kappes, Markus Speth, Gerhard Reinelt, Christoph Schnorr
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Combinatorial Optimization, Playing the Game of 2048

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: Zihan Zhou, Hailin Jin, Yi Ma
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Novel View Synthesis, Video Stabilization

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: Ramakrishna Kakarala, Prabhu Kaliamoorthi, Vittal Premachandran
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6
Tasks: Retrieval

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: Chao-Yeh Chen, Kristen Grauman
Date: 2013-06-01 00:00:00
Proceeding: CVPR 2013 6

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.