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Globally optimal pixel labeling algorithms for tree metrics

Globally optimal pixel labeling algorithms for tree metrics

引用

conference on computer vision and pattern recognition (CVPR)

作者： Pedro F. Felzenszwalb Gyula Pap Éva Tardos Ramin Zabih University of Chicago USA Cornell University USA

We consider pixel labeling problems where the label set forms a tree, and where the observations are also labels. Such problems arise in feature-space analysis with a very large label set, for instance in color image segmentation. In this case a tree of labels can be constructed via hierarchical clustering of the observations. This leads to an obvious distance function between two labels, namely their distance within the tree; such tree metrics have been extensively studied outside of computer vision. We provide fast algorithms that use graph cuts to exactly minimize the energy function for pixel labeling problems with tree metrics. Our work substantially improves a facility location algorithm of Kolen, which is impractical for large label sets L since it requires O(|L|) min cuts on large graphs. Our main technical contribution is a new ordering of swap moves that reduces the running time to the equivalent of O(log |L|) min cuts; as a result, we can handle realistic-sized color images in a few seconds.

关键词： Labeling Pixel Tree graphs computer vision Noise reduction Image analysis Image color analysis Image segmentation Clustering algorithms Markov random fields

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On-line semi-supervised multiple-instance boosting

On-line semi-supervised multiple-instance boosting

引用

conference on computer vision and pattern recognition (CVPR)

作者： Bernhard Zeisl Christian Leistner Amir Saffari Horst Bischof Institute for Computer Graphics and Vision Technical University of Graz Austria Computer Vision and Geometry Group ETH Zurich Switzerland

A recent dominating trend in tracking called tracking-by-detection uses on-line classifiers in order to redetect objects over succeeding frames. Although these methods usually deliver excellent results and run in real-time they also tend to drift in case of wrong updates during the self-learning process. Recent approaches tackled this problem by formulating tracking-by-detection as either one-shot semi-supervised learning or multiple instance learning. Semi-supervised learning allows for incorporating priors and is more robust in case of occlusions while multiple-instance learning resolves the uncertainties where to take positive updates during tracking. In this work, we propose an on-line semi-supervised learning algorithm which is able to combine both of these approaches into a coherent framework. This leads to more robust results than applying both approaches separately. Additionally, we introduce a combined loss that simultaneously uses labeled and unlabeled samples, which makes our tracker more adaptive compared to previous on-line semi-supervised methods. Experimentally, we demonstrate that by using our semi-supervised multiple-instance approach and utilizing robust learning methods, we are able to outperform state-of-the-art methods on various benchmark tracking videos.

关键词： Boosting Semisupervised learning Robustness Particle tracking computer vision computer graphics Computational geometry Uncertainty Learning systems Videos

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A diffusion approach to seeded image segmentation

A diffusion approach to seeded image segmentation

引用

conference on computer vision and pattern recognition (CVPR)

作者： Juyong Zhang Jianmin Zheng Jianfei Cai Nanyang Technological University Singapore

Seeded image segmentation is a popular type of supervised image segmentation in computer vision and image processing. Previous methods of seeded image segmentation treat the image as a weighted graph and minimize an energy function on the graph to produce a segmentation. In this paper, we propose to conduct the seeded image segmentation according to the result of a heat diffusion process in which the seeded pixels are considered to be the heat sources and the heat diffuses on the image starting from the sources. After the diffusion reaches a stable state, the image is segmented based on the pixel temperatures. It is also shown that our proposed framework includes the RandomWalk algorithm for image segmentation as a special case which diffuses only along the two coordinate axes. To better control diffusion, we propose to incorporate the attributes (such as the geometric structure) of the image into the diffusion process, yielding an anisotropic diffusion method for image segmentation. The experiments show that the proposed anisotropic diffusion method usually produces better segmentation results. In particular, when the method is tested using the groundtruth dataset of Microsoft Research Cambridge (MSRC), an error rate of 4.42% can be achieved, which is lower than the reported error rates of other state-of-the-art algorithms.

关键词： Image segmentation Pixel Diffusion processes Anisotropic magnetoresistance Temperature computer vision Image processing Error analysis Equations Process control

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Support vector regression for multi-view gait recognition based on local motion feature selection

Support vector regression for multi-view gait recognition ba...

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conference on computer vision and pattern recognition (CVPR)

作者： Worapan Kusakunniran Qiang Wu Jian Zhang Hongdong Li School of Computer Science and Engineering University of New South Wales Australia NICTA Australia School of Computing and Communications Sydney University of Technology Australia Research School of Information Sciences and Engineering Australian National University Australia

Gait is a well recognized biometric feature that is used to identify a human at a distance. However, in real environment, appearance changes of individuals due to viewing angle changes cause many difficulties for gait recognition. This paper re-formulates this problem as a regression problem. A novel solution is proposed to create a View Transformation Model (VTM) from the different point of view using Support Vector Regression (SVR). To facilitate the process of regression, a new method is proposed to seek local Region of Interest (ROI) under one viewing angle for predicting the corresponding motion information under another viewing angle. Thus, the well constructed VTM is able to transfer gait information under one viewing angle into another viewing angle. This proposal can achieve view-independent gait recognition. It normalizes gait features under various viewing angles into a common viewing angle before similarity measurement is carried out. The extensive experimental results based on widely adopted benchmark dataset demonstrate that the proposed algorithm can achieve significantly better performance than the existing methods in literature.

关键词： Cameras Humans Legged locomotion Rendering (computer graphics) Matrix decomposition Biometrics Image reconstruction Image recognition Linear discriminant analysis computer science

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Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes

Modeling pixel process with scale invariant local patterns f...

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conference on computer vision and pattern recognition (CVPR)

作者： Shengcai Liao Guoying Zhao Vili Kellokumpu Matti Pietikäinen Stan Z. Li Center for Biometrics and Security Research Chinese Academy of Sciences China Machine Vision Group University of Oulu Finland

Background modeling plays an important role in video surveillance, yet in complex scenes it is still a challenging problem. Among many difficulties, problems caused by illumination variations and dynamic backgrounds are the key aspects. In this work, we develop an efficient background subtraction framework to tackle these problems. First, we propose a scale invariant local ternary pattern operator, and show that it is effective for handling illumination variations, especially for moving soft shadows. Second, we propose a pattern kernel density estimation technique to effectively model the probability distribution of local patterns in the pixel process, which utilizes only one single LBP-like pattern instead of histogram as feature. Third, we develop multimodal background models with the above techniques and a multiscale fusion scheme for handling complex dynamic backgrounds. Exhaustive experimental evaluations on complex scenes show that the proposed method is fast and effective, achieving more than 10% improvement in accuracy compared over existing state-of-the-art algorithms.

关键词： Layout Lighting Encoding Background noise Noise figure Kernel Probability distribution Histograms Biometrics National security

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Novel observation model for probabilistic object tracking

Novel observation model for probabilistic object tracking

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conference on computer vision and pattern recognition (CVPR)

作者： Dawei Liang Qingming Huang Hongxun Yao Shuqiang Jiang Rongrong Ji Wen Gao School of Computer Science and Technology Harbin Institute of Technology Harbin China Institute of Computing Technology Chinese Academy and Sciences Beijing China National Engineering Laboratory for Video Technology Peking University Beijing China

Treating visual object tracking as foreground and background classification problem has attracted much attention in the past decade. Most methods adopt mean shift or brute force search to perform object tracking on the generated probability map, which is obtained from the classification results; however, performing probabilistic object tracking on the probability map is almost unexplored. This paper proposes a novel observation model which is suitable to perform this task. The observation model considers both region and boundary cues on the probability map, and can be computed very efficiently by using the integral image data structure. Extensive experiments are carried out on several challenging image sequences, which include abrupt motion change, background clutter, partial occlusion, and significant appearance change. Quantitative experiments are further performed with several related trackers on a public benchmark dataset. The experimental results demonstrate the effectiveness of the proposed approach.

关键词： Particle filters Particle tracking Integral equations Data structures Boosting Linear discriminant analysis computer vision computer science Laboratories Image sequences

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Many-to-one contour matching for describing and discriminating object shape

Many-to-one contour matching for describing and discriminati...

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conference on computer vision and pattern recognition (CVPR)

作者： Praveen Srinivasan Qihui Zhu Jianbo Shi GRASP Laboratory University of Pennsylvania Philadelphia PA USA

We present an object recognition system that locates an object, identifies its parts, and segments out its contours. A key distinction of our approach is that we use long, salient, bottom-up image contours to learn object shape, and to achieve object detection with the learned shape. Most learning methods rely on one-to-one matching of contours to a model. However, bottom-up image contours often fragment unpredictably. We resolve this difficulty by using many-to-one matching of image contours to a model. To learn a descriptive object shape model, we combine bottom-up contours from a few representative images. The goal is to allow most of the contours in the training images to be many-to-one matched to the model. For detection, our challenges are inferring the object contours and part locations, in addition to object location. Because the locations of object parts and matches of contours are not annotated, they appear as latent variables during training. We use the latent SVM learning formulation to discriminatively tune the many-to-one matching score using the max-margin criterion. We evaluate on the challenging ETHZ shape categories dataset and outperform all existing methods.

关键词： Shape Image segmentation Surface reconstruction Isosurfaces Visualization Data mining Reconstruction algorithms Image converters Embedded computing computer vision

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Automatic image annotation using group sparsity

Automatic image annotation using group sparsity

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conference on computer vision and pattern recognition (CVPR)

作者： Shaoting Zhang Junzhou Huang Yuchi Huang Yang Yu Hongsheng Li Dimitris N. Metaxas Department of Computer Science Rutgers University Piscataway NJ USA Department of Computer Science and Engineering Lehigh University Bethlehem PA USA

Automatically assigning relevant text keywords to images is an important problem. Many algorithms have been proposed in the past decade and achieved good performance. Efforts have focused upon model representations of keywords, but properties of features have not been well investigated. In most cases, a group of features is preselected, yet important feature properties are not well used to select features. In this paper, we introduce a regularization based feature selection algorithm to leverage both the sparsity and clustering properties of features, and incorporate it into the image annotation task. A novel approach is also proposed to iteratively obtain similar and dissimilar pairs from both the keyword similarity and the relevance feedback. Thus keyword similarity is modeled in the annotation framework. Numerous experiments are designed to compare the performance between features, feature combinations and regularization based feature selection methods applied on the image annotation task, which gives insight into the properties of features in the image annotation task. The experimental results demonstrate that the group sparsity based method is more accurate and stable than others.

关键词： Histograms computer science Testing Focusing Clustering algorithms Iterative algorithms Feedback Image recognition Noise robustness Image coding

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Discriminative K-SVD for dictionary learning in face recognition

Discriminative K-SVD for dictionary learning in face recogni...

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conference on computer vision and pattern recognition (CVPR)

作者： Qiang Zhang Baoxin Li Computer Science and Engineering Arizona State University Tempe AZ USA

In a sparse-representation-based face recognition scheme, the desired dictionary should have good representational power (i.e., being able to span the subspace of all faces) while supporting optimal discrimination of the classes (i.e., different human subjects). We propose a method to learn an over-complete dictionary that attempts to simultaneously achieve the above two goals. The proposed method, discriminative K-SVD (D-KSVD), is based on extending the K-SVD algorithm by incorporating the classification error into the objective function, thus allowing the performance of a linear classifier and the representational power of the dictionary being considered at the same time by the same optimization procedure. The D-KSVD algorithm finds the dictionary and solves for the classifier using a procedure derived from the K-SVD algorithm, which has proven efficiency and performance. This is in contrast to most existing work that relies on iteratively solving sub-problems with the hope of achieving the global optimal through iterative approximation. We evaluate the proposed method using two commonly-used face databases, the Extended YaleB database and the AR database, with detailed comparison to 3 alternative approaches, including the leading state-of-the-art in the literature. The experiments show that the proposed method outperforms these competing methods in most of the cases. Further, using Fisher criterion and dictionary incoherence, we also show that the learned dictionary and the corresponding classifier are indeed better-posed to support sparse-representation-based recognition.

关键词： Dictionaries Face recognition Databases Iterative algorithms Image coding Optimization methods Image recognition computer vision Face detection Feature extraction

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A Steiner tree approach to efficient object detection

A Steiner tree approach to efficient object detection

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conference on computer vision and pattern recognition (CVPR)

作者： Olga Russakovsky Andrew Y. Ng Computer Science Department University of Stanford USA

We propose an approach to speeding up object detection, with an emphasis on settings where multiple object classes are being detected. Our method uses a segmentation algorithm to select a small number of image regions on which to run a classifier. Compared to the classical sliding window approach, this results in a significantly smaller number of rectangles examined, and thus significantly faster object detection. Further, in the multiple object class setting, we show that the computational cost of proposing candidate regions can be amortized across objects classes, resulting in an additional speedup. At the heart of our approach is a reduction to a directed Steiner tree optimization problem, which we solve approximately in order to select the segmentation algorithm parameters. The solution gives a small set of segmentation strategies that can be shared across object classes. Compared to the sliding window approach, our method results in two orders of magnitude fewer regions considered, and significant (10-15×) running time speedups on challenging object detection datasets (LabelMe and StreetScenes) while maintaining comparable detection accuracy.

关键词： Object detection Image segmentation Heart Pixel Layout Runtime Algorithm design and analysis Merging computer science Computational efficiency

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