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Advances in Intelligent Systems and Computing 2013年 212卷 985-993页

作者： Chen, Ying Tang, Jin Luo, Bin School of Computer Science and Technology Anhui University Hefei 230601 China Key Lab of Industrial Image Processing and Analysis of Anhui Province Hefei 230039 China

ISBN: (纸本)9783642375019

image structure representation is a vital technique in the image recognition. A novel image representation and recognition method based on directed complex network is proposed in this paper. Firstly, the key points are extracted from an image as the nodes to construct an initial complete undirected complex network. Then, the k-nearest neighbor evolution method is designed to form a series of directed networks. At last, the feature descriptor of the image is constructed by concatenating the structure features of each directed network to finally achieve image recognition. Experimental results demonstrate that the proposed method outperforms the traditional methods in image recognition and can describe the structure of images more effectively. © Springer-Verlag Berlin Heidelberg 2013.

关键词： image recognition

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A Local Elitism Based Membrane Evolutionary Algorithm for Point Pattern Matching

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and Computing 2013年 212卷 873-882页

作者： Ding, Zhuanlian Tang, Jin Zhang, Xingyi Luo, Bin School of Computer Science and Technology Anhui University Hefei 230601 Anhui China Key Lab of Industrial Image Processing and Analysis of Anhui Province Hefei 230039 Anhui China

ISBN: (纸本)9783642375019

Point pattern matching is a fundamental problem in computer vision and pattern recognition. Membrane computing is an emergent branch of bio-inspired computing, which provides a novel idea to solve computationally hard problems. In this paper, a new point pattern matching algorithm with local elitism strategy is proposed based on membrane computing models. Local elitism strategy is used to keep good correspondences of point pattern matching found during the search, so the matching rate and the convergence speed are improved. Five heuristic mutation rules are introduced to avoid the local optimum. Experiment results on both synthetic data and real world data illustrate that the proposed algorithm is of higher matching rate and better stability. © Springer-Verlag Berlin Heidelberg 2013.

关键词： Membranes

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Video Summarization by Robust Low-Rank Subspace Segmentation

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and Computing 2013年 212卷 929-937页

作者： Tu, Zhengzheng Sun, Dengdi Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei 230039 China School of Computer Science and Technology Anhui University Hefei 230601 China Key Lab of Industrial Image Processing and Analysis of Anhui Province Hefei 230039 China

ISBN: (纸本)9783642375019

Video summarization provides condensed and succinct representations of the content of a video stream. A static storyboard summarization approach based on robust low-rank subspace segmentation is proposed in this paper. Firstly, video frames are represented as multi-dimensional vectors, and then embedded into a group of affine subspaces using low-rank representation according to the content similarity of the frames in the same subspace. Secondly, a series of subspaces are segmented based on the Normalized Cuts algorithm. The video summary is finally generated by choosing key frames from the significant subspaces and ranking these key frames in temporal order. The experimental results demonstrate that the proposed summarization algorithm can produce crucial key frames and effectively reduce the visual content redundancy in summary comparing with the conventional approaches. © Springer-Verlag Berlin Heidelberg 2013.

关键词： Vectors

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An efficient algorithm for feature selection with feature correlation

An efficient algorithm for feature selection with feature co...

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3rd Sino-Foreign-Interchange Workshop on Intelligent Science and Intelligent Data Engineering, IScIDE 2012

作者： Huang, Li-Li Tang, Jin Chen, Si-Bao Ding, Chris Luo, Bin School of Computer Science and Technology Anhui University 230601 China Key Lab of Industrial Image Processing and Analysis of Anhui Province Hefei 230039 Anhui China

ISBN: (纸本)9783642366680

Feature selection is an important component of many machine learning applications. In this paper, we propose a new robust feature selection method for multi-class multi-label learning. In particular, feature correlation is added into the sparse learning of feature selection so that we can learn the feature correlation and do feature selection simultaneously. An efficient algorithm is introduced with rapid convergence. Our regression based objective makes the feature selection process more efficient. Experiments on benchmark data sets illustrate that the proposed method outperforms many state-of-the-art feature selection methods. © Springer-Verlag 2013.

关键词： Feature Selection

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image representation and recognition based on directed complex network model

Image representation and recognition based on directed compl...

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8th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2013

作者： Chen, Ying Tang, Jin Luo, Bin School of Computer Science and Technology Anhui University Hefei230601 China Key Lab of Industrial Image Processing and Analysis of Anhui Province Hefei230039 China

ISBN: (纸本)9783642375019

关键词： image recognition

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A local elitism based membrane evolutionary algorithm for point pattern matching

A local elitism based membrane evolutionary algorithm for po...

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8th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2013

作者： Ding, Zhuanlian Tang, Jin Zhang, Xingyi Luo, Bin School of Computer Science and Technology Anhui University HefeiAnhui230601 China Key Lab of Industrial Image Processing and Analysis of Anhui Province HefeiAnhui230039 China

ISBN: (纸本)9783642375019

关键词： Membranes

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Video summarization by robust low-rank subspace segmentation

Video summarization by robust low-rank subspace segmentation

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8th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2013

作者： Tu, Zhengzheng Sun, Dengdi Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei230039 China School of Computer Science and Technology Anhui University Hefei230601 China Key Lab of Industrial Image Processing and Analysis of Anhui Province Hefei230039 China

ISBN: (纸本)9783642375019

关键词： Vectors

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Robust integrated locally linear embedding

Robust integrated locally linear embedding

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7th Chinese Conference on Biometric Recognition, CCBR 2012

作者： Zhang, Li-Li Xie, Ying Luo, Bin Ding, Chris Tang, Jin Key Lab of Industrial Image Processing & Analysis of Anhui Province Hefei 230039 China School of Computer Science and Technology Anhui University Hefei 230601 China CSE Department University of Texas at Arlington Arlington TX 76019 United States of America

ISBN: (纸本)9783642355059

Many real life applications often bring much high-dimensional and noise-contaminated data from different sources. In this paper, we consider de-noising as well as dimensionality reduction by proposing a novel method named Robust Integrated Locally Linear Embedding. The method combines the two steps in LLE into a single framework and deals with de-noising by solving a l 2,1-l 2 mixed norm based optimization problem. We also derive an efficient algorithm to build the proposed model. Extensive experiments demonstrate that the proposed method is more suitable to exhibit relationship among data points, and has visible improvement in de-noising, embedding and clustering tasks. © 2012 Springer-Verlag.

关键词： Reduction

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Corrigendum to “Graph structure analysis based on complex network” [Digital Signal processing 22 (5) (2012) 713–725]

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Digital Signal processing 2013年第4期23卷 1332-1332页

作者： Jin Tang Bo Jiang Chin-Chen Chang Bin Luo School of Computer Science and Technology Anhui University Hefei 230039 Anhui China Key lab of Image Processing & Analysis of Anhui Province Hefei 230039 Anhui China Department of Information Engineering and Computer Science Feng Chia University Taiwan

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A new shape based segmentation framework using statistical and variational methods

A new shape based segmentation framework using statistical a...

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IEEE International Conference on image processing

作者： Melih S. Aslan Hossam Abdelmunim Aly A. Farag Ben Arnold Eslam Mostafa Ping Xiang Computer Vision and Image Processing Lab University of Louisville Louisville KY USA Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA Computer and Systems Engineering Department Ain Shams University Cairo Egypt Image Analysis Inc Columbia KY USA

In this paper, we propose a new shape based segmentation and registration of the vertebral bodies (VBs) in clinical computed tomography (CT) images. The VB and surrounding organs have very close gray level information and there are no strong edges in some CT images. To overcome these challenges, image appearance and shape information of VBs are used. There are three phases of our experiments: i) the detection of the VB region using the Matched filter, ii) initial segmentation using the graph cuts which integrates the intensity and spatial interaction models, iii) registration of the shape priors and initially segmented region to obtain the final segmentation. Preliminary results show that our proposed algorithm gives very encouraging results and can solve many segmentation and registration problems.

关键词： image segmentation Shape Training Computed tomography Bones Accuracy Vectors

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