pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Simultaneous Orthogonal Basis Clustering Feature Selection
Authors: Dongyoon Han, Junmo Kim
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Clustering, feature selection

In this paper, we propose a novel unsupervised feature selection method: Simultaneous Orthogonal basis Clustering Feature Selection (SOCFS). To perform feature selection on unlabeled data effectively, a regularized regression-based formulation with a new type of target matrix is designed. The target matrix captures latent cluster centers of the projected data points by performing orthogonal basis clustering, and then guides the projection matrix to select discriminative features. Unlike the recent unsupervised feature selection methods, SOCFS does not explicitly use the pre-computed local structure information for data points represented as additional terms of their objective functions, but directly computes latent cluster information by the target matrix conducting orthogonal basis clustering in a single unified term of the proposed objective function. It turns out that the proposed objective function can be minimized by a simple optimization algorithm. Experimental results demonstrate the effectiveness of SOCFS achieving the state-of-the-art results with diverse real world datasets.
Space-Time Tree Ensemble for Action Recognition
Authors: Shugao Ma, Leonid Sigal, Stan Sclaroff
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Action Recognition, Clustering, Temporal Action Localization

Human actions are, inherently, structured patterns of body movements. We explore ensembles of hierarchical spatio-temporal trees, discovered directly from training data, to model these structures for action recognition. The hierarchical spatio-temporal trees provide a robust mid-level representation for actions. However, discovery of frequent and discriminative tree structures is challenging due to the exponential search space, particularly if one allows partial matching. We address this by first building a concise action vocabulary via discriminative clustering. Using the action vocabulary we then utilize tree mining with subsequent tree clustering and ranking to select a compact set of highly discriminative tree patterns. We show that these tree patterns, alone, or in combination with shorter patterns (action words and pair-wise patterns) achieve state-of-the-art  performance on two challenging datasets: UCF Sports and HighFive. Moreover, trees learned on HighFive are used in recognizing two action classes in a different dataset, Hollywood3D, demonstrating the potential for cross-dataset generality of the trees our approach discovers.
Subgraph Decomposition for Multi-Target Tracking
Authors: Siyu Tang, Bjoern Andres, Miykhaylo Andriluka, Bernt Schiele
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Clustering

Tracking multiple targets in a video, based on a finite set of detection hypotheses, is a persistent problem in computer vision. A common strategy for tracking is to first select hypotheses spatially and then to link these over time while maintaining disjoint path constraints. In crowded scenes multiple hypotheses will often be similar to each other making selection of optimal links an unnecessary hard optimization problem due to the sequential treatment of space and time. Embracing this observation, we propose to link and cluster plausible detections jointly across space and time. Specifically, we state multi-target tracking as a Minimum Cost Subgraph Multicut Problem. Evidence about pairs of detection hypotheses is incorporated whether the detections are in the same frame, neighboring frames or distant frames. This facilitates long-range re-identification and within-frame clustering. Results for published benchmark sequences demonstrate the superiority of this approach.
Understanding Image Structure via Hierarchical Shape Parsing
Authors: Xian-Ming Liu, Rongrong Ji, Changhu Wang, Wei Liu, Bineng Zhong, Thomas S. Huang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

Exploring image structure is a long-standing yet important research subject in the computer vision community. In this paper, we focus on understanding image structure inspired by the "simple-to-complex" biological evidence. A hierarchical shape parsing strategy is proposed to partition and organize image components into a hierarchical structure in the scale space. To improve the robustness and flexibility of image representation, we further bundle the image appearances into hierarchical parsing trees. Image descriptions are subsequently constructed by performing a structural pooling, facilitating efficient matching between the parsing trees. We leverage the proposed hierarchical shape parsing to study two exemplar applications including edge scale refinement and unsupervised "objectness" detection. We show competitive parsing performance comparing to the state-of-the-arts in above scenarios with far less proposals, which thus demonstrates the advantage of the proposed parsing scheme.
Coarse-To-Fine Region Selection and Matching
Authors: Yanchao Yang, Zhaojin Lu, Ganesh Sundaramoorthi
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

We present a new approach to wide baseline matching. We propose to use a hierarchical decomposition of the image domain and coarse-to-fine selection of regions to match. In contrast to interest point matching methods, which sample salient regions to reduce the cost of comparing all regions in two images, our method eliminates regions systematically to achieve efficiency. One advantage of our approach is that it is not restricted to covariant salient regions, which is too restrictive under large viewpoint and leads to few corresponding regions. Affine invariant matching of regions in the hierarchy is achieved efficiently by a coarse-to-fine search of the affine space. Experiments on two benchmark datasets shows that our method finds more correct correspondence of the image (with fewer false alarms) than other wide baseline methods on large viewpoint change.
Label Consistent Quadratic Surrogate Model for Visual Saliency Prediction
Authors: Yan Luo, Yongkang Wong, Qi Zhao
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Prediction, Saliency Prediction

Recently, an increasing number of works have proposed to learn visual saliency by leveraging human fixations. However, the collection of human fixations is time consuming and the existing eye tracking datasets are generally small when compared with other domains. Thus, it contains a certain degree of dataset bias due to the large image variations (e.g., outdoor scenes vs. emotion-evoking images). In the learning based saliency prediction literature, most models are trained and evaluated within the same dataset and cross dataset validation is not yet a common practice. Instead of directly applying model learned from another dataset in cross dataset fashion, it is better to transfer the prior knowledge obtained from one dataset to improve the training and prediction on another. In addition, since new datasets are built and shared in the community from time to time, it would be good not to retrain the entire model when new data are added. To address these problems, we proposed a new learning based saliency model, namely Label Consistent Quadratic Surrogate algorithm, which employs an iterative online algorithm to learn a sparse dictionary with label consistent constraint. The advantages of the proposed model are three-folds: (1) the quadratic surrogate function guarantees convergence at each iteration, (2) the label consistent constraint enforces the predicted sparse code to be discriminative, and (3) the online properties enable the proposed algorithm to adapt existing model with new data without retraining. As shown in this work, the proposed saliency model achieves better performance than the state-of-the-art saliency models.
Subgraph Matching Using Compactness Prior for Robust Feature Correspondence
Authors: Yumin Suh, Kamil Adamczewski, Kyoung Mu Lee
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Graph Matching

Feature correspondence plays a central role in various computer vision applications. It is widely formulated as a graph matching problem due to its robust performance under challenging conditions, such as background clutter, object deformation and repetitive patterns. A variety of fast and accurate algorithms  have been proposed for graph matching. However, most of them focus on improving the recall of the solution while rarely considering its precision, thus inducing a solution with numerous outliers. To address both precision and recall feature correspondence should rather be formulated as a subgraph matching problem. This paper proposes a new subgraph matching formulation which uses a compactness prior, an additional constraint that prefers sparser solutions and effectively eliminates outliers. To solve the new optimization problem, we propose a meta-algorithm based on Markov chain Monte Carlo. By constructing Markov chain on the restricted search space instead of the original solution space, our method approximates the solution effectively. The experiments indicate that our proposed formulation and algorithm significantly improve the baseline performance under challenging conditions when both outliers and deformation noise are present.
Multihypothesis Trajectory Analysis for Robust Visual Tracking
Authors: Dae-Youn Lee, Jae-Young Sim, Chang-Su Kim
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Visual Tracking

The notion of multihypothesis trajectory analysis (MTA) for robust visual tracking is proposed in this work. We employ multiple component trackers using texture, color, and illumination invariant features, respectively. Each component tracker traces a target object forwardly and then backwardly over a time interval. By analyzing the pair of the forward and backward trajectories, we measure the robustness of the component tracker. To this end, we extract the geometry similarity, the cyclic weight, and the appearance similarity from the forward and backward trajectories. We select the optimal component tracker to yield the maximum robustness score, and use its forward trajectory as the final tracking result. Experimental results show that the proposed MTA tracker improves the robustness and the accuracy of tracking, outperforming the state-of-the-art trackers on a recent benchmark dataset.
Object Detection by Labeling Superpixels
Authors: Junjie Yan, Yinan Yu, Xiangyu Zhu, Zhen Lei, Stan Z. Li
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: General Classification, Object, object-detection, Object Detection, Object Proposal Generation, Superpixels

Object detection is always conducted by object proposal generation and classification sequentially. This paper handles object detection in a superpixel oriented manner instead of the proposal oriented. Specially, this paper takes object detection as a multi-label superpixel labeling problem by minimizing an energy function. It uses the data cost term to capture the appearance, smooth cost term to encode the spatial context and label cost term to favor compact detection. The data cost is learned through a convolutional neural network and the parameters in the labeling model are learned through a structural SVM. Compared with proposal generation and classification based methods, the proposed superpixel labeling method can naturally detect objects missed by proposal generation step and capture the global image context to infer the overlapping objects.   The proposed method shows its advantage in Pascal VOC and ImageNet. Notably, it performs better than the ImageNet ILSVRC2014 winner GoogLeNet (45.0% V.S. 43.9% in mAP) with much shallower and fewer CNNs.
Fast 2D Border Ownership Assignment
Authors: Ching Teo, Cornelia Fermuller, Yiannis Aloimonos
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Boundary Detection

A method for efficient border ownership assignment in 2D images is proposed. Leveraging on recent advances using Structured Random Forests (SRF) for boundary detection, we impose a novel border ownership structure that detects both boundaries and border ownership at the same time. Key to this work are features that predict ownership cues from 2D images. To this end, we use several different local cues: shape, spectral properties of boundary patches, and semi-global grouping cues that are indicative of perceived depth. For shape, we use HoG-like descriptors that encode local curvature (convexity and concavity). For spectral properties, such as extremal edges, we first learn an orthonormal basis spanned by the top K eigenvectors via PCA over common types of contour tokens. For grouping, we introduce a novel mid-level descriptor that captures patterns near edges and indicates ownership information of the boundary. Experimental results over a subset of the Berkeley Segmentation Dataset (BSDS) and the NYU Depth V2 dataset show that our method's performance exceeds current state-of-the-art multi-stage approaches that use more complex features.
From Single Image Query to Detailed 3D Reconstruction
Authors: Johannes L. Schonberger, Filip Radenovic, Ondrej Chum, Jan-Michael Frahm
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: 3D Reconstruction, Retrieval

Structure-from-Motion for unordered image collections has significantly advanced in scale over the last decade. This impressive progress can be in part attributed to the introduction of efficient retrieval methods for those systems. While this boosts scalability, it also limits the amount of detail that the large-scale reconstruction systems are able to produce. In this paper, we propose a joint reconstruction and retrieval system that maintains the scalability of large-scale Structure-from-Motion systems while also recovering the often lost ability of reconstructing fine details of the scene. We demonstrate our proposed method on a large-scale dataset of 7.4 million images downloaded from the Internet.
Fast and Flexible Convolutional Sparse Coding
Authors: Felix Heide, Wolfgang Heidrich, Gordon Wetzstein
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: General Classification

Convolutional sparse coding (CSC) has become an increasingly important tool in machine learning and computer vision. Image features can be learned and subsequently used for classification and reconstruction tasks. As opposed to patch-based methods, convolutional sparse coding operates on whole images, thereby seamlessly capturing the correlation between local neighborhoods. In this paper, we propose a new approach to solving CSC problems and show that our method converges significantly faster and also finds better solutions than the state of the art. In addition, the proposed method is the first efficient approach to allow for proper boundary conditions to be imposed and it also supports feature learning from incomplete data as well as general reconstruction problems.
Pairwise Geometric Matching for Large-Scale Object Retrieval
Authors: Xinchao Li, Martha Larson, Alan Hanjalic
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Computational Efficiency, Geometric Matching, Image Retrieval, Object, Retrieval

Spatial verification is a key step in boosting the performance of object-based image retrieval. It serves to eliminate unreliable correspondences between salient points in a given pair of images and is typically performed by analyzing the consistency of spatial transformations between the image regions involved in individual correspondences. In this paper, we consider the pairwise geometric relations between correspondences and propose a strategy to incorporate these relations at significantly reduced computational cost, which makes it suitable for large-scale object retrieval. In addition, we combine the information on geometric relations from both the individual correspondences and pairs of correspondences to further improve the verification accuracy. Experimental results on three reference datasets show that the proposed approach results in a substantial performance improvement compared to the existing methods, without making concessions regarding computational efficiency.
Data-Driven Sparsity-Based Restoration of JPEG-Compressed Images in Dual Transform-Pixel Domain
Authors: Xianming Liu, Xiaolin Wu, Jiantao Zhou, Debin Zhao
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Quantization

Arguably the most common cause of image degradation is compression.  This papers presents a novel approach to restoring JPEG-compressed images.  The main innovation is in the approach of exploiting residual redundancies of JPEG code streams and sparsity properties of latent images.  The restoration is a sparse coding process carried out jointly in the DCT and pixel domains.  The prowess of the proposed approach is directly restoring DCT coefficients of the latent image to prevent the spreading of quantization errors into the pixel domain, and at the same time using on-line machine-learnt local spatial features to regulate the solution of the underlying inverse problem.  Experimental results are encouraging and show the promise of the new approach in significantly improving the quality of DCT-coded images.
TVSum: Summarizing Web Videos Using Titles
Authors: Yale Song, Jordi Vallmitjana, Amanda Stent, Alejandro Jaimes
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Descriptive, Image Retrieval, Unsupervised Video Summarization, Video Summarization

Video summarization is a challenging problem in part because knowing which part of a video is important requires prior knowledge about its main topic. We present TVSum, an unsupervised video summarization framework that uses title-based image search results to find visually important shots. We observe that a video title is often carefully chosen to be maximally descriptive of its main topic, and hence images related to the title can serve as a proxy for important visual concepts of the main topic. However, because titles are free-formed, unconstrained, and often written ambiguously, images searched using the title can contain noise (images irrelevant to video content) and variance (images of different topics). To deal with this challenge, we developed a novel co-archetypal analysis technique that learns canonical visual concepts shared between video and images, but not in either alone, by finding a joint-factorial representation of two data sets. We introduce a new benchmark dataset, TVSum50, that contains 50 videos and their shot-level importance scores annotated via crowdsourcing. Experimental results on two datasets, SumMe and TVSum50, suggest our approach produces superior quality summaries compared to several recently proposed approaches.
Single Image Super-Resolution From Transformed Self-Exemplars
Authors: Jia-Bin Huang, Abhishek Singh, Narendra Ahuja
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Image Super-Resolution, Super-Resolution

Self-similarity based super-resolution (SR) algorithms are able to produce visually pleasing results without extensive training on external databases. Such algorithms exploit the statistical prior that patches in a natural image tend to recur within and across scales of the same image. However, the internal dictionary obtained from the given image may not always be sufficiently expressive to cover the textural appearance variations in the scene. In this paper, we extend self-similarity based SR to overcome this drawback. We expand the internal patch search space by allowing geometric variations. We do so by explicitly localizing planes in the scene and using the detected perspective geometry to guide the patch search process. We also incorporate additional affine transformations to accommodate local shape variations. We propose a compositional model to simultaneously handle both types of transformations. We extensively evaluate the performance in both urban and natural scenes. Even without using any external training databases, we achieve significantly superior results on urban scenes, while maintaining comparable performance on natural scenes as other state-of-the-art SR algorithms.
Constrained Planar Cuts - Object Partitioning for Point Clouds
Authors: Markus Schoeler, Jeremie Papon, Florentin Worgotter
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Clustering, Object

While humans can easily separate unknown objects into meaningful parts, recent segmentation methods can only achieve similar partitionings by training on human-annotated ground-truth data. Here we introduce a bottom-up method for segmenting 3D point clouds into functional parts which does not require supervision and achieves equally good results. Our method uses local concavities as an indicator for inter-part boundaries. We show that this criterion is efficient to compute and generalizes well across different object classes. The algorithm employs a novel locally constrained geometrical boundary model which proposes greedy cuts through a local concavity graph. Only planar cuts are considered and evaluated using a cost function, which rewards cuts orthogonal to concave edges. Additionally, a local clustering constraint is applied to ensure the partitioning only affects relevant locally concave regions. We evaluate our algorithm on recordings from an RGB-D camera as well as the Princeton Segmentation Benchmark, using a fixed set of parameters across all object classes. This stands in stark contrast to most reported results which require either knowing the number of parts or annotated ground-truth for learning. Our approach outperforms all existing bottom-up methods (reducing the gap to human performance by up to 50%) and achieves scores similar to top-down data-driven approaches.
A Weighted Sparse Coding Framework for Saliency Detection
Authors: Nianyi Li, Bilin Sun, Jingyi Yu
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Saliency Detection, Stereo Matching, Stereo Matching Hand, Superpixels

There is an emerging interest on using high-dimensional datasets beyond 2D images in saliency detection. Examples include 3D data based on stereo matching and Kinect sensors and more recently 4D light field data. However, these techniques adopt very different solution frameworks, in both type of features and procedures on using them. In this paper, we present a unified saliency detection framework for handling heterogenous types of input data. Our approach builds dictionaries using data-specific features. Specifically, we first select a group of potential background superpixels to build a primitive non-saliency dictionary. We then prune the outliers in the dictionary and test on the remaining superpixels to iteratively refine the dictionary. Comprehensive experiments show that our approach universally outperforms the state-of-the-art solution on all 2D, 3D and 4D data.
Handling Motion Blur in Multi-Frame Super-Resolution
Authors: Ziyang Ma, Renjie Liao, Xin Tao, Li Xu, Jiaya Jia, Enhua Wu
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Image Reconstruction, Multi-Frame Super-Resolution, Super-Resolution

Ubiquitous motion blur easily fails multi-frame super-resolution (MFSR). Our method proposed in this paper tackles this issue by optimally searching least blurred pixels in MFSR. An EM framework is proposed to guide residual blur estimation and high-resolution image reconstruction. To suppress noise, we employ a family of sparse penalties as natural image priors, along with an effective solver. Theoretical analysis is performed on how and when our method works. The relationship between estimation errors of motion blur and the quality of input images is discussed. Our method produces sharp and higher-resolution results given input of challenging low-resolution noisy and blurred sequences.