OpenRaiser/Intern-Atlas huggingface dataset:
Reconstruction of super-resolution lung 4D-CT using patch-based sparse representation
Authors: Yu Zhang 0064, Guorong Wu 0001, Pew-Thian Yap, Qianjin Feng 0003, Jun Lian, Wufan Chen, Dinggang Shen
Venue: CVPR
Year: 2012

4D-CT plays an important role in lung cancer treatment. However, due to the inherent high-dose exposure associated with CT, dense sampling along superior-inferior direction is often not practical. As a result, artifacts such as lung vessel discontinuity and partial volume are typical in 4D-CT images and might mislead dose administration in radiation therapy. In this paper, we present a novel patch-based technique for super-resolution enhancement of the 4D-CT images along the superior-inferior direction. Our working premise is that the anatomical information that is missing at one particular phase can be recovered from other phases. Based on this assumption, we employ a patch-based mechanism for guided reconstruction of super-resolution axial slices. Specifically, to reconstruct each targeted super-resolution slice for a CT image at a particular phase, we agglomerate a dictionary of patches from images of all other phases in the 4D-CT sequence. Then we perform a sparse combination of the patches in this dictionary to reconstruct details of a super-resolution patch, under constraint of similarity to the corresponding patches in the neighboring slices. By iterating this procedure over all possible patch locations, a superresolution 4D-CT image sequence with enhanced anatomical details can be eventually reconstructed. Our method was extensively evaluated using a public dataset. In all experiments, our method outperforms the conventional linear and cubic-spline interpolation methods in terms of preserving image details and suppressing misleading artifacts.
Power SVM: Generalization with exemplar classification uncertainty
Authors: Weiyu Zhang, Stella X. Yu, Shang-Hua Teng
Venue: CVPR
Year: 2012

The human vision tends to recognize more variants of a distinctive exemplar. This observation suggests that discriminative power of training exemplars could be utilized for shaping a desirable global classifier that generalizes maximally from a few exemplars. We propose to derive classification uncertainty for each exemplar, using a local classification task to separate the exemplar from those in other categories. We then design a global classifier by incorporating these uncertainties into constraints on the classifier margins. We show through the dual form that the classification criterion can be interpreted as finding closest points between convex hulls in the feature space augmented by classification uncertainty. We call this scheme Power SVM (as in Power Diagram), since each exemplar is no longer a singular point in the feature space, but a super-point with its own governing power in the classifier space. We test Power SVM on digit recognition, indoor-outdoor categorization, and large-scale scene classification tasks. It shows consistent improvement over SVM and uncertainty weighted SVM, especially when the number of training exemplars is small.
QsRank: Query-sensitive hash code ranking for efficient ∊-neighbor search
Authors: Xiao Zhang, Lei Zhang 0001, Heung-Yeung Shum
Venue: CVPR
Year: 2012

Although binary hash code-based image indexing methods have been recently developed for large-scale applications, the problem of ranking such hash codes has been barely studied. In this paper, we propose a query sensitive ranking algorithm (QsRank) to rank PCA-based hash codes for the ∊-neighbor search problem. The QsRank algorithm takes the target neighborhood radius ∊ and the raw feature of a given query as input, and models the statistical properties of the target ∊-neighbors in the space of hash codes. Unlike the Hamming distance, the proposed algorithm does not compress query points to hash codes. Therefore, it suffers less information loss and is more effective than Hamming distance-based approaches. Based on the QsRank method, we developed an efficient indexing structure and retrieval algorithm for large-scale ∊-neighbor search. Evaluations on two datasets of 10 million web images and 10 million SIFT descriptors demonstrate that the proposed retrieval system achieves higher accuracy with less memory cost and faster speed.
Robust Non-negative Graph Embedding: Towards noisy data, unreliable graphs, and noisy labels
Authors: Hanwang Zhang, Zheng-Jun Zha, Shuicheng Yan, Meng Wang 0001, Tat-Seng Chua
Venue: CVPR
Year: 2012

Non-negative data factorization has been widely used recently. However, existing techniques, such as Non-negative Graph Embedding (NGE), often suffer from noisy data, unreliable graphs, and noisy labels, which are commonly encountered in real-world applications. To address these issues, in this paper, we propose a Robust Non-negative Graph Embedding (RNGE) framework. The joint sparsity in both graph embedding and reconstruction endues the robustness of RNGE. We develop an elegant multiplicative updating solution that can solve RNGE efficiently and prove the convergence rigourously. RNGE is robust to unreliable graphs, as well as both sample and label noises in training data. Moreover, RNGE provides a general formulation such that all the algorithms unified with the graph embedding framework can be easily extended to obtain their robust non-negative solutions. We conduct extensive experiments on four real-world datasets and compared the proposed RNGE to NGE and other representative non-negative data factorization and subspace learning methods. The experimental results demonstrate the effectiveness and robustness of RNGE.
On the dimensionality of video bricks under varying illumination
Authors: Youdong Zhao, Xi Song, Yunde Jia
Venue: CVPR
Year: 2012

Illumination models of the image set of an object (e.g., human face) under varying lighting conditions have been either empirically or analytically explored. However, the theoretical dimensionality of video bricks of an object under varying illumination is still unknown. In this paper, we focus on this question concretely and give both analytical and empirical results. We derive the theoretical upper bound of the dimensionality of video bricks by investigating the analytical formula of appearance changes due to motion variables of light sources. Theoretical results show in real-world scenes video bricks of an object under varying illumination could be expressed well by a low-dimensional linear subspace. Empirical results of the principal component analysis on the YaleB Face database and our video database are consistent with the theoretical results completely. The application of the low-dimensional linear models of video bricks is demonstrated by the foreground detection task in visual surveillance with drastic illumination changes.
Per-pixel translational symmetry detection, optimization, and segmentation
Authors: Peng Zhao, Lei Yang 0006, Honghui Zhang, Long Quan
Venue: CVPR
Year: 2012

We present a novel method for translational symmetry detection, optimization, and symmetry object segmentation in façade images. Unlike most previous methods, our detection algorithm accumulates pixel-level correspondence in translation space. Thus it does not rely on feature point detection and handles patterns with low repetition counts. To improve the robustness with multiple interfering symmetries, we introduce an image-space global optimization, which resolves multiple per-pixel symmetry lattices. We then propose a learning-based method that generates refined segmentation of foreground symmetry objects of arbitrary shapes, with the aid of the per-pixel symmetry information. Our proposed method is accurate, robust and efficient as demonstrated by an extensive evaluation using a large façade image database.
MAP-MRF inference based on extended junction tree representation
Authors: Yun Zheng, Pei Chen 0001, Jiang-Zhong Cao
Venue: CVPR
Year: 2012

Maximum a-posteriori (MAP) inference in Markov random fields (MRF) is an important topic in machine learning, computer vision and other fields. Message passing algorithms based on linear programming (LP) relaxation are powerful tools for the MAP-MRF problems. However, current message passing algorithms are usually based on simple subgraphs, resulting in slow convergence, local optimum and untightness of the LP relaxation for many problems. By extending the junction tree representation, we propose a general convergent message passing algorithm, which can work on arbitrary tractable bounded treewidth subgraphs. In the extended junction tree representation, the minimization and summation operators are commutable so that the proposed algorithm based on the extended junction tree is guaranteed to converge. Based on the treewidth-2 decomposition, better performance of the proposed algorithm is demonstrated on stereo matching, optical flow and panorama.
Transfer re-identification: From person to set-based verification
Authors: Wei-Shi Zheng 0001, Shaogang Gong, Tao Xiang 0002
Venue: CVPR
Year: 2012

Solving the person re-identification problem has become important for understanding people's behaviours in a multicamera network of non-overlapping views. In this work, we address the problem of re-identification from a set-based verification perspective. More specifically, we have a small set of target people on a watch list (a set) and we aim to verify whether a query image of a person is on this watch list. This differs from the existing person re-identification problem in that the probe is verified against a small set of known people but requires much higher degree of verification accuracy with very limited sampling data for each candidate in the set. That is, rather than recognising everybody in the scene, we consider identifying a small set of target people against non-target people when there is only a limited number of target training samples and a large number of unlabelled (unknown) non-target samples available. To this end, we formulate a transfer learning framework for mining discriminant information from non-target people data to solve the watch list set verification problem. Based on the proposed approach, we introduce the concepts of multi-shot and one-shot verifications. We also design new criteria for evaluating the performance of the proposed transfer learning method against the i-LIDS and ETHZ data sets.
Practical low-rank matrix approximation under robust L1-norm
Authors: Yinqiang Zheng, Guangcan Liu, Shigeki Sugimoto, Shuicheng Yan, Masatoshi Okutomi
Venue: CVPR
Year: 2012

A great variety of computer vision tasks, such as rigid/nonrigid structure from motion and photometric stereo, can be unified into the problem of approximating a low-rank data matrix in the presence of missing data and outliers. To improve robustness, the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm measurement has long been recommended. Unfortunately, existing methods usually fail to minimize the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -based nonconvex objective function sufficiently. In this work, we propose to add a convex trace-norm regularization term to improve convergence, without introducing too much heterogenous information. We also customize a scalable first-order optimization algorithm to solve the regularized formulation on the basis of the augmented Lagrange multiplier (ALM) method. Extensive experimental results verify that our regularized formulation is reasonable, and the solving algorithm is very efficient, insensitive to initialization and robust to high percentage of missing data and/or outliers <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Generalizing Wiberg algorithm for rigid and nonrigid factorizations with missing components and metric constraints
Authors: Yinqiang Zheng, Shigeki Sugimoto, Shuicheng Yan, Masatoshi Okutomi
Venue: CVPR
Year: 2012

In spite of intensive endeavor over decades, rigid and nonrigid factorizations under metric constraints, possibly in the presence of missing components, remain to be very challenging. In this work, we try to break the hard nut by generalizing to these problems the Wiberg algorithm, one of the most successful solutions for unconstrained bilinear factorization. To properly handle missing components, we advocate a bilinear factorization formulation with an extra mean vector. In spirit of the Wiberg algorithm, we first propose an efficient and initialization-insensitive algorithm for unconstrained factorization, posterior correction of whose solution offers reasonable initialization for metric upgrade. For factorization with metric constraints, we reformulate it into an unconstrained problem through quaternion parametrization, which merges elegantly into our unconstrained factorization algorithm. Extensive experiment results verify that our proposed methods are fast, accurate and robust to high percentage of missing components.
Learning active facial patches for expression analysis
Authors: Lin Zhong 0002, Qingshan Liu 0001, Peng Yang 0001, Bo Liu 0005, Junzhou Huang, Dimitris N. Metaxas
Venue: CVPR
Year: 2012

In this paper, we present a new idea to analyze facial expression by exploring some common and specific information among different expressions. Inspired by the observation that only a few facial parts are active in expression disclosure (e.g., around mouth, eye), we try to discover the common and specific patches which are important to discriminate all the expressions and only a particular expression, respectively. A two-stage multi-task sparse learning (MTSL) framework is proposed to efficiently locate those discriminative patches. In the first stage MTSL, expression recognition tasks, each of which aims to find dominant patches for each expression, are combined to located common patches. Second, two related tasks, facial expression recognition and face verification tasks, are coupled to learn specific facial patches for individual expression. Extensive experiments validate the existence and significance of common and specific patches. Utilizing these learned patches, we achieve superior performances on expression recognition compared to the state-of-the-arts.
Robust plane-based structure from motion
Authors: Zihan Zhou 0001, Hailin Jin, Yi Ma 0001
Venue: CVPR
Year: 2012

We introduce a new approach to structure and motion recovery directly from one or more large planes in the scene. When such a plane exists, we demonstrate how to automatically detect and track it robustly and consistently over a long video sequence, and how to efficiently self-calibrate the camera using the homographies induced by this plane. We build a complete structure from motion system which does not use any additional off-the-plane information about the scene, and show its advantage over conventional systems in handling two important issues which often occur in real world videos, namely, the plane degeneracy and the dynamic foreground problems. Experimental results on a variety of real video sequences verify the effectiveness and efficiency of our system.
Markov Weight Fields for face sketch synthesis
Authors: Hao Zhou 0010, Zhanghui Kuang, Kwan-Yee Kenneth Wong
Venue: CVPR
Year: 2012

Great progress has been made in face sketch synthesis in recent years. State-of-the-art methods commonly apply a Markov Random Fields (MRF) model to select local sketch patches from a set of training data. Such methods, however, have two major drawbacks. Firstly, the MRF model used cannot synthesize new sketch patches. Secondly, the optimization problem in solving the MRF is NP-hard. In this paper, we propose a novel Markov Weight Fields (MWF) model that is capable of synthesizing new sketch patches. We formulate our model into a convex quadratic programming (QP) problem to which the optimal solution is guaranteed. Based on the Markov property of our model, we further propose a cascade decomposition method (CDM) for solving such a large scale QP problem efficiently. Experimental results on the CUHK face sketch database and celebrity photos show that our model outperforms the common MRF model used in other state-of-the-art methods.
Factorized graph matching
Authors: Feng Zhou 0002, Fernando De la Torre
Venue: CVPR
Year: 2012

Graph matching plays a central role in solving correspondence problems in computer vision. Graph matching problems that incorporate pair-wise constraints can be cast as a quadratic assignment problem (QAP). Unfortunately, QAP is NP-hard and many algorithms have been proposed to solve different relaxations. This paper presents factorized graph matching (FGM), a novel framework for interpreting and optimizing graph matching problems. In this work we show that the affinity matrix can be factorized as a Kronecker product of smaller matrices. There are three main benefits of using this factorization in graph matching: (1) There is no need to compute the costly (in space and time) pair-wise affinity matrix; (2) The factorization provides a taxonomy for graph matching and reveals the connection among several methods; (3) Using the factorization we derive a new approximation of the original problem that improves state-of-the-art algorithms in graph matching. Experimental results in synthetic and real databases illustrate the benefits of FGM. The code is available at http://humansensing.cs.cmu.edu/fgm.
Robust stereo with flash and no-flash image pairs
Authors: Changyin Zhou, Alejandro J. Troccoli, Kari Pulli
Venue: CVPR
Year: 2012

We propose a new stereo technique using a pair of flash and no-flash stereo images that is both efficient and robust in handling occlusion boundaries. Our work is motivated by the observation that the brightness variations introduced by the flash can provide a robust cue for establishing stereo matches at occlusion boundaries. This photometric cue is computed per pixel, and though on its own is not robust to reliably resolve depth, it can provide a new discriminant to support patch-based stereo matching algorithms. Our experiments using a hand-held Fujifilm W3 3D camera show satisfying stereo performance over a variety of scenes, including several outdoor scenes.
Automatic mitral leaflet tracking in echocardiography by outlier detection in the low-rank representation
Authors: Xiaowei Zhou 0001, Can Yang 0002, Weichuan Yu
Venue: CVPR
Year: 2012

Tracking the mitral valve leaflet in an ultrasound sequence is a challenging task because of the poor image quality and fast and irregular leaflet motion. Previous algorithms usually applied standard segmentation methods based on edges, object intensity and anatomical information to segment the mitral leaflet in static frames. However, they are limited in practical applications due to the requirement of manual input for initialization or large annotated datasets for training. In this paper we present a completely automatic and unsupervised algorithm for mitral leaflet detection and tracking. We demonstrate that the image sequence of a cardiac cycle can be well approximated with a low-rank matrix, except for the mitral leaflet region with fast motion and tissue deformation. Based on this difference, we propose to track the mitral leaflet by detecting contiguous outliers in the low-rank representation. With this formulation, the leaflet is tracked using the motion cue, but the complicated motion computation is avoided. To the best of our knowledge, the proposed algorithm is the first unsupervised method for mitral leaflet tracking. The algorithm was tested on both 2D and 3D echocardiography, which achieved accurate segmentation with an average distance of 0.87 ± 0.42mm compared to the manual tracing.
Unsupervised object class discovery via saliency-guided multiple class learning
Authors: Jun-Yan Zhu, Jiajun Wu 0001, Yichen Wei, Eric I-Chao Chang, Zhuowen Tu
Venue: CVPR
Year: 2012

Discovering object classes from images in a fully unsupervised way is an intrinsically ambiguous task; saliency detection approaches however ease the burden on unsupervised learning. We develop an algorithm for simultaneously localizing objects and discovering object classes via bottom-up (saliency-guided) multiple class learning (bMCL), and make the following contributions: (1) saliency detection is adopted to convert unsupervised learning into multiple instance learning, formulated as bottom-up multiple class learning (bMCL); (2) we utilize the Discriminative EM (DiscEM) to solve our bMCL problem and show DiscEM's connection to the MIL-Boost method[34]; (3) localizing objects, discovering object classes, and training object detectors are performed simultaneously in an integrated framework; (4) significant improvements over the existing methods for multi-class object discovery are observed. In addition, we show single class localization as a special case in our bMCL framework and we also demonstrate the advantage of bMCL over purely data-driven saliency methods.
Non-negative low rank and sparse graph for semi-supervised learning
Authors: Liansheng Zhuang, Haoyuan Gao, Zhouchen Lin, Yi Ma 0001, Xin Zhang 0051, Nenghai Yu
Venue: CVPR
Year: 2012

Constructing a good graph to represent data structures is critical for many important machine learning tasks such as clustering and classification. This paper proposes a novel non-negative low-rank and sparse (NNLRS) graph for semi-supervised learning. The weights of edges in the graph are obtained by seeking a nonnegative low-rank and sparse matrix that represents each data sample as a linear combination of others. The so-obtained NNLRS-graph can capture both the global mixture of subspaces structure (by the low rankness) and the locally linear structure (by the sparseness) of the data, hence is both generative and discriminative. We demonstrate the effectiveness of NNLRS-graph in semi-supervised classification and discriminative analysis. Extensive experiments testify to the significant advantages of NNLRS-graph over graphs obtained through conventional means.
The role of image understanding in contour detection
Authors: C. Lawrence Zitnick, Devi Parikh
Venue: CVPR
Year: 2012

Many cues have been proposed for contour detection or image segmentation. These include low-level image gradients to high-level information such as the identity of the objects in the scene or 3D depth understanding. While state-of-the-art approaches have been incorporating more cues, the relative importance of the cues is unclear. In this paper, we examine the relative importance of low-, mid- and high-level cues to gain a better understanding of their role in detecting object contours in an image. To accomplish this task, we conduct numerous human studies and compare their performance to several popular segmentation and contour detection machine approaches. Our findings suggest that the current state-of-the-art contour detection algorithms perform as well as humans using low-level cues. We also find evidence that the recognition of objects, but not occlusion information, leads to improved human performance. Moreover, when objects are recognized by humans, their contour detection performance increases over current machine algorithms. Finally, mid-level cues appear to offer a larger performance boost than high-level cues such as recognition.