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Co-Clustering Image Features and Semantic Concepts

Co-Clustering Image Features and Semantic Concepts

引用

IEEE International conference on Image Processing

作者： Manjeet Rege Ming Dong Farshad Fotouhi Department of Computer Science Machine Vision & Pattern Recognition Laboratory Wayne State University Detroit MI USA Database & Multimedia Systems Group Wayne State University Detroit MI USA

In this paper, we present a novel idea of co-clustering image features and semantic concepts. We accomplish this by modelling user feedback logs and low-level features using a bipartite graph. Our experiments demonstrate that (1) incorporating semantic information achieves better image clustering and (2) feature selection in co-clustering narrows the semantic gap, thus enabling efficient image retrieval.

关键词： Feedback Image retrieval Bipartite graph Image databases Clustering algorithms Machine vision pattern recognition Multimedia databases Spatial databases Multimedia systems

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Nonlinear Manifold Clustering By Dimensionality

Nonlinear Manifold Clustering By Dimensionality

引用

International conference on pattern recognition

作者： Wenbo Cao R. Haralick Computer Science Pattern Recognition Laboratory The Graduate Center City University of New York New York NY USA

ISBN: (纸本)0769525210

Because of variable dependence, high dimensional data typically have much lower intrinsic dimensionality than the number of its variables. Hence high dimensional data can be expected to lie in (nonlinear) lower dimensional manifold. In this paper, we describe a nonlinear manifold clustering algorithm. By connecting data vectors with their neighbors in feature space, we construct a neighborhood graph from given set data vectors. Furthermore, geometrical invariance, namely dimensionality, are extracted from the neighborhood of vectors, and used to facilitate the clustering procedure. In addition, we discuss a latent model for data cluster descriptions and an EM algorithm to find such descriptions. Preliminary experiments illustrate that this new algorithm can be used to explore the nonlinear structure of data

关键词： Clustering algorithms Machine learning algorithms pattern recognition Partitioning algorithms Vectors computer science Joining processes Data mining Machine learning computer vision

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Symmetric Pixel-Group Based Stereo Matching for Occlusion Handling

Symmetric Pixel-Group Based Stereo Matching for Occlusion Ha...

引用

International conference on pattern recognition

作者： Xiuzhi Zhou Runsheng Wang ATR Laboratory National University of Defense Technology Changsha Hunan China

ISBN: (纸本)0769525210

In this paper, we propose a symmetric pixel-group stereo model for handling occlusion in a segment-based style. Firstly, both images are segmented based on color, disparity, and the segments of the other image sequencely. Then the uniqueness constraint is embodied in pixel-group level. Finally, a symmetric belief propagation (BP) optimization framework is used to find correspondence and occlusions simultaneously. Results obtained for benchmark indicate that the proposed method is able to compete with the state-of-the-art-algorithms

关键词： Image segmentation Stereo vision Pixel Iterative algorithms Layout Minimization methods Belief propagation Optimization methods computer vision Application software

来源：评论

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Motion from Focus

Motion from Focus

引用

International conference on pattern recognition

作者： H.Q. Huy Viet M. Sato H.T. Tanaka Department of Human and Computer Intelligence Ritsumeikan University Japan Precision and Intelligence Laboratory Tokyo Institute of Technology Japan

Based on the triangulation method, the 3D motion of an object can be completely recognized by a stereo camera. However, the question whether or not the 3D motion of an object can be completely recognized by a motionless/fixed monocular camera is the yet-unanswered question. In this paper we propose a method using a motionless monocular camera of which the focus is changed in cycle to recognize the absolute 3D motion of an object

关键词： Cameras Focusing Lenses Robot vision systems Competitive intelligence Layout Humans Image sensors Laboratories Stereo vision

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Visually Significant Dynamics for Watershed Segmentation

Visually Significant Dynamics for Watershed Segmentation

引用

International conference on pattern recognition

作者： J. Climent A. Sanfeliu Computer Engineering and Automatic Control Department Technical University of Catalonia Spain

The watershed transform is a powerful tool for segmentation once we can deal with oversegmentation. To solve the oversegmentation problem, hierarchical approaches are considered in order to retain the most significant regions of the image at different scales. The dynamics of the regional minima have been used to build this hierarchy. In this paper we present a new measure for computing the dynamics of the minima based on human perception of shapes. The described technique solves the major drawbacks of the hierarchical segmentations based on contrast dynamics or volume dynamics

关键词： Image segmentation Shape measurement Humans Area measurement Automatic control computer vision Object recognition Volume measurement Visual perception Topology

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Robust Recursive Learning for Foreground Region Detection in Videos with Quasi-Stationary Backgrounds

Robust Recursive Learning for Foreground Region Detection in...

引用

International conference on pattern recognition

作者： A. Tavakkoli M. Nicolescu G. Bebis Computer Vision Laboratory University of Nevada Reno NV USA

Detecting regions of interest in video sequences is the most important task in many high level video processing applications. In this paper a robust technique based on recursive learning of video background and foreground models is presented. Our contributions can be described along four directions. First, a recursive learning scheme is developed to build pixel models based on their colors. Second, we generate background and foreground models to enforce the temporal consistency of detected foregrounds. Third, we exploit dependencies between pixel colors to insure that the model is not restricted to using only independent features. Finally, an adaptive pixel-wise criterion is proposed that incorporates different spatial situations in the scene

关键词： Robustness Layout Cameras Convergence computer vision Laboratories Video sequences Application software Surveillance Kernel

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Detection of moving cast shadows using image orthogonal transform

Detection of moving cast shadows using image orthogonal tran...

引用

International conference on pattern recognition

作者： Wei Zhang Xiang Zhong Fang Yi Xu Institute of Image Communication and Information Processing Shanghai Jiaotong University Shanghai China

ISBN: (纸本)0769525210

In many image and computer vision applications, shadows interfere with fundamental tasks such as moving objects segmentation and tracking. In this paper, a novel method is proposed to detect the moving cast shadows in the scene. The normalized coefficients of orthogonal transform of image block are proved to be illumination invariant and are used to classify moving shadows and foreground objects. Five kinds of orthogonal transform: DCT, DFT, Haar transform, SVD and Hadamard transform, are utilized in our work to detect moving cast shadows. Experimental results show that the proposed method succeeds in detecting moving cast shadows within indoor and outdoor environments

关键词： Layout computer vision Application software Object segmentation Lighting Discrete cosine transforms Image color analysis Object detection Image communication Information processing

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Global-to-Local Non-Rigid Shape Registration

Global-to-Local Non-Rigid Shape Registration

引用

International conference on pattern recognition

作者： Hui Chen B. Bhanu Center for Research in Intelligent Systems University of California Riverside CA USA

Non-rigid shape registration is an important issue in computer vision. In this paper we propose a novel global-to-local procedure for aligning non-rigid shapes. The global similarity transformation is obtained based on the corresponding pairs found by matching shape context descriptors. The local deformation is performed within an optimization formulation, in which the bending energy of thin plate spline transformation is incorporated as a regularization term to keep the structure of the model shape preserved under the shape deformation. The optimization procedure drives the initial global registration towards the target shape that results in the one-to-one correspondence between the model and target shape. Experimental results demonstrate the effectiveness of the proposed approach

关键词： Shape Deformable models computer vision Spline Clustering algorithms Iterative algorithms Intelligent systems Application software Target tracking Robustness

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Tensor Discriminant Analysis for View-based Object recognition

Tensor Discriminant Analysis for View-based Object Recogniti...

引用

International conference on pattern recognition

作者： Yong Wang Shaogang Gong Department of Computer Science Queen''s Mary University of London UK

In this paper, we use a general Mth order tensor discriminant analysis approach (Tao et al. 2005) for view based object recognition. This method is an extension of the 2D image coding technique (Shashua and Levin, 2001) to general Mth order tensors for discriminant analysis, and has good convergence property. We demonstrate the performance advantages of this approach over existing techniques using experiments on the COIL-100 and the ETH-80 datasets. Specifically, our experimental results on ETH-80 show the particular strength of this tensor discriminant analysis method when only a small number of training samples with big intra-class variation are available

关键词： Tensile stress Object recognition Image coding Image analysis Convergence Machine learning Snow Color computer science computer vision

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Gait recognition Using Multiple Projections

Gait Recognition Using Multiple Projections

引用

International conference on Automatic Face and Gesture recognition

作者： M. Ekinci Computer Vision Laboratory Department of Computer Engineering Karadeniz Technical University Turkey

This paper presents a new method for automatic gait recognition based on analyzing the multiple projections to silhouette using principal components analysis (PCA). Binarized silhouette of a motion object is represented by 1-D signals which are the basic image features called the distance vectors. The distance vectors are differences between the bounding box and silhouette, and extracted using four projections to silhouette. Based on normalized correlation on the distance vectors, gait cycle estimation is first performed to extract the gait cycle. Second, an eigenspace transformation based on PCA is applied to time-varying distance vectors and the statistical distance based supervised pattern classification is then performed in the lower-dimensional eigenspace for human identification. A fusion strategy developed is finally executed to produce final decision. Experimental results on four databases demonstrate that the right person in top two matches 100% of the times for the cases where training and testing sets corresponds to the same walking styles, and in top three-four matches 100% of the times for training and testing sets corresponds to the different walking styles

关键词： Legged locomotion Principal component analysis computer vision Humans Testing Signal analysis Pixel pattern classification Databases pattern recognition

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