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检索条件"任意字段=MIPPR 2007: Pattern Recognition and Computer Vision"

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Clustering Web images with multi-modal features

Clustering Web images with multi-modal features

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15th ACM International Conference on Multimedia, MM'07

作者： Rege, Manjeet Dong, Ming Hua, Jing Machine Vision Pattern Recognition Lab. Department of Computer Science Wayne State University Detroit MI 48202 United States Graphics and Imaging Lab. Department of Computer Science Wayne State University Detroit MI 48202 United States

ISBN: (纸本)9781595937025

Web image clustering has drawn significant attention in the research community recently. However, not much work has been done in using multi-modal information for clustering Web images. In this paper, we address the problem of Web image clustering by simultaneous integration of visual and textual features from a graph partitioning perspective. In particular, we modelled visual features, images, and words from the surrounding text of the images using a tripartite graph. This graph is actually considered as a fusion of two bipartite graphs that are partitioned simultaneously by the proposed Consistent Isoperimetric High-order Co-clustering(CIHC) framework. Although a similar approach has been adopted before, the main contribution of this work lies in the computational efficiency, quality in Web image clustering and scalability to large image repositories that CIHC is able to achieve. We demonstrate this through experimental results performed on real Web images. Copyright 2007 ACM.

关键词： Image analysis

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pattern Theory

引用

2007年

作者： Ulf Grenander Michael I. Miller

ISBN: (数字)9780191523113

ISBN: (纸本)9780198505709

pattern Theory: From Representation to Inference provides a comprehensive and accessible overview of the modern challenges in signal, data and pattern analysis in speech recognition, computational linguistics, image analysis and computer vision. Aimed at graduate students in biomedical engineering, mathematics, computer science and electrical engineering with a good background in mathematics and probability, the text includes numerous exercises and an extensivebibliography. Additional resources including extended proofs, selected solutions and examples are available on a companion *** book commences with a short overview of pattern theory and the basics of statistics and estimation theory. Chapters 3-6 discuss the role of representation of patterns via conditioning structure and Chapters 7 and 8 examine the second central component of pattern theory: groups of geometric transformation applied to the representation of geometric objects. Chapter 9 moves into probabilistic structures in the continuum, studying random processes and random fields indexed over subsets of Rn,and Chapters 10, 11 continue with transformations and patterns indexed over the continuum. Chapters 12-14 extend from the pure representations of shapes to the Bayes estimation of shapes and their parametric representation. Chapters 15 and 16 study the estimation of infinite dimensional shape in thenewly emergent field of Computational Anatomy, and finally Chapters 17 and 18 look at inference, exploring random sampling approaches for estimation of model order and parametric representing of shapes.

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Kernel Fukunaga-Koontz Transform Subspaces For Enhanced Face recognition

Kernel Fukunaga-Koontz Transform Subspaces For Enhanced Face...

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Yung-hui Li Marios Savvides Carnegie Mellon University Pittsburgh PA USA

Traditional linear Fukunaga-Koontz transform (FKT) (F. Fukunaga and W. Koontz, 1970) is a powerful discriminative subspaces building approach. Previous work has successfully extended FKT to be able to deal with small-sample-size. In this paper, we extend traditional linear FKT to enable it to work in multi-class problem and also in higher dimensional (kernel) subspaces and therefore provide enhanced discrimination ability. We verify the effectiveness of the proposed kernel Fukunaga-Koontz transform by demonstrating its effectiveness in face recognition applications; however the proposed non-linear generalization can be applied to any other domain specific problems.

关键词： Kernel Face recognition Principal component analysis pattern recognition Independent component analysis Support vector machines Support vector machine classification Signal processing Data mining Feature extraction

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Saliency Detection: A Spectral Residual Approach

Saliency Detection: A Spectral Residual Approach

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Xiaodi Hou Liqing Zhang Department of Computer Science Shanghai Jiaotong University Shanghai China

The ability of human visual system to detect visual saliency is extraordinarily fast and reliable. However, computational modeling of this basic intelligent behavior still remains a challenge. This paper presents a simple method for the visual saliency detection. Our model is independent of features, categories, or other forms of prior knowledge of the objects. By analyzing the log-spectrum of an input image, we extract the spectral residual of an image in spectral domain, and propose a fast method to construct the corresponding saliency map in spatial domain. We test this model on both natural pictures and artificial images such as psychological patterns. The result indicate fast and robust saliency detection of our method.

关键词： Object detection Computational modeling Humans Visual system Image analysis Object recognition Machine vision Redundancy Image coding Statistical distributions

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Hierarchical Matching of Deformable Shapes

Hierarchical Matching of Deformable Shapes

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Conference on computer vision and pattern recognition (CVPR)

作者： Pedro F. Felzenszwalb Joshua D. Schwartz University of Chicago USA

We describe a new hierarchical representation for two-dimensional objects that captures shape information at multiple levels of resolution. This representation is based on a hierarchical description of an object's boundary and can be used in an elastic matching framework, both for comparing pairs of objects and for detecting objects in cluttered images. In contrast to classical elastic models, our representation explicitly captures global shape information. This leads to richer geometric models and more accurate recognition results. Our experiments demonstrate classification results that are significantly better than the current state-of-the-art in several shape datasets. We also show initial experiments in matching shapes to cluttered images.

关键词： Shape Image segmentation Object detection Solid modeling Deformable models Humans computer vision Computational complexity Testing MPEG 7 Standard

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TrackSense: Infrastructure free precise iNdoor positioning using projected patterns

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5th International Conference on Pervasive Computing, PERVASIVE 2007

作者： Köhler, Moritz Patel, Shwetak N. Summet, Jay W. Stuntebeck, Erich P. Abowd, Gregory D. Institute for Pervasive Computing Department of Computer Science ETH Zurich 8092 Zurich Switzerland College of Computing GVU Center Georgia Institute of Technology 85 5th Street NW Atlanta GA 30332-0280 United States

ISBN: (纸本)9783540720362

While commercial solutions for precise indoor positioning exist, they are costly and require installation of additional infrastructure, which limits opportunities for widespread adoption. Inspired by robotics techniques of Simultaneous Localization and Mapping (SLAM) and computer vision approaches using structured light patterns, we propose a self-contained solution to precise indoor positioning that requires no additional environmental infrastructure. Evaluation of our prototype, called TrackSense, indicates that such a system can deliver up to 4 cm accuracy with 3 cm precision in rooms up to five meters squared, as well as 2 degree accuracy and 1 degree precision on orientation. We explain the design and performance characteristics of our prototype and demonstrate a feasible miniaturization that supports applications that require a single device localizing itself in a space. We also discuss extensions to locate multiple devices and limitations of this approach. © Springer-Verlag Berlin Heidelberg 2007.

关键词： pattern recognition

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High Distortion and Non-Structural Image Matching via Feature Co-occurrence

High Distortion and Non-Structural Image Matching via Featur...

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Conference on computer vision and pattern recognition (CVPR)

作者： Xi Chen Tat-Jen Cham School of Computer Engineering Nanyang Technological University Singapore

We propose a novel approach for determining if a pair of images match each other under the effect of a high-distortion transformation or non-structural relation. The co-occurrence statistics between features across a pair of images are learned from a training set comprising matched and mismatched image pairs - these are expressed in the form of a cross-feature ratio table. The proposed method does not require feature-to-feature correspondences, but instead identifies and exploits feature co-occurrences that are able to provide discriminative result from the transformation. The method not only allows for the matching of test image pairs that have substantially different visual content as compared to those present in the training set, but also caters for transformations and relations that do not preserve image structure.

关键词： Image matching Training data Testing Object recognition Filters Statistics Image recognition Humans pattern matching Books

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Learning Dynamic Event Descriptions in Image Sequences

Learning Dynamic Event Descriptions in Image Sequences

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Conference on computer vision and pattern recognition (CVPR)

作者： Harini Veeraraghavan Nikolaos Papanikolopoulos Paul Schrater Department of Computer Science and Engineering University of Minnesota USA

Automatic detection of dynamic events in video sequences has a variety of applications including visual surveillance and monitoring, video highlight extraction, intelligent transportation systems, video summarization, and many more. Learning an accurate description of the various events in real-world scenes is challenging owing to the limited user-labeled data as well as the large variations in the pattern of the events. pattern differences arise either due to the nature of the events themselves such as the spatio-temporal events or due to missing or ambiguous data interpretation using computer vision methods. In this work, we introduce a novel method for representing and classifying events in video sequences using reversible context-free grammars. The grammars are learned using a semi-supervised learning method. More concretely, by using the classification entropy as a heuristic cost function, the grammars are iteratively learned using a search method. Experimental results demonstrating the efficacy of the learning algorithm and the event detection method applied to traffic video sequences are presented.

关键词： Image sequences Video sequences Event detection Intelligent transportation systems Surveillance computerized monitoring Data mining Layout computer vision Semisupervised learning

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Approximate Nearest Subspace Search with Applications to pattern recognition

Approximate Nearest Subspace Search with Applications to Pat...

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Conference on computer vision and pattern recognition (CVPR)

作者： Ronen Basri Tal Hassner Lihi Zelnik-Manor Weizmann Institute of Science Rehovot Israel California Institute of Technology Pasadena CA USA

Linear and affine subspaces are commonly used to describe appearance of objects under different lighting, viewpoint, articulation, and identity. A natural problem arising from their use is - given a query image portion represented as a point in some high dimensional space - find a subspace near to the query. This paper presents an efficient solution to the approximate nearest subspace problem for both linear and affine subspaces. Our method is based on a simple reduction to the problem of nearest point search, and can thus employ tree based search or locality sensitive hashing to find a near subspace. Further speedup may be achieved by using random projections to lower the dimensionality of the problem. We provide theoretical proofs of correctness and error bounds of our construction and demonstrate its capabilities on synthetic and real data. Our experiments demonstrate that an approximate nearest subspace can be located significantly faster than the exact nearest subspace, while at the same time it can find better matches compared to a similar search on points, in the presence of variations due to viewpoint, lighting etc.

关键词： pattern recognition Nearest neighbor searches Space technology Error correction computer vision Application software Image databases Search problems Art Machine vision

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Image threshold using A-IFSs based on bounded histograms

Image threshold using A-IFSs based on bounded histograms

引用

12th World Congress of the International-Fuzzy-Systems-Association

作者： Couto, Pedro Bustince, Humberto Filipe, Vitor Barrenechea, Edurne Pagola, Miguel Melo-Pinto, Pedro Univ Tras Os Montes & Alto Douro CETAV Ap 1014 P-5001911 Vila Real Portugal Univ Publica Navarra Dept Automat & Computac Pamplona 31006 Spain

ISBN: (纸本)9783540729174

Atanassov's intuitionistic fuzzy sets (A-IFSs) have been used recently to determine the optimal threshold value for gray-level image segmentation [1]. Atanassov's intuitionistic fuzzy index values are used for representing the unknowledge/ignorance of an expert on determining whether a pixel of the image belongs to the background or the object of the image. This optimal global threshold of the image is computed automatically, regardless of the actual image analysis process. Although global optimal thresholding techniques give good results under experimental conditions, when dealing with real images having several objects and the segmentation purpose is to point out some application-specific information, one should use heuristic techniques in order to obtain better thresholding results. This paper introduces an evolution of the above mentioned technique intended for use with such images. The proposed approach takes into account the image and segmentation specificities by using a two-step procedure, with a restricted set of the image gray-levels. Preliminary experimental results and comparison with other methods are presented.

关键词： fuzzy sets theory applications atanassov's intuitionistic fuzzy sets (A-IFSs) computer vision pattern recognition digital image processing

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