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2013 International Conference on computer,Networks and Communication Engineering(ICCNCE 2013)

作者： Zhengwei Guo Lin Zhu Department of Computer and Information Engineering Institute of Image Processing and Pattern Recognition Henan University Department of Computer and Information Technology Shangqiu Normal University

ISBN: (纸本)9781629932170

An effective approach for coverage holes was proposed, called Distance-assistant Coverage Hole Parching (DACHP) strategy. DACHP strategy would achieve the purpose of patching coverage holes using redundant sensor nodes within the wireless sensor networks. Simulation results show, compared with DFNFP and random algorithm, for the same size and shape coverage hole, DACHP can awaken five redundant nodes to patch 92.19% of hole, yet DFNFP needs awaken nine redundant nodes and just patch 90.28%. It can be seen that DFNFP not only awaken four extra nodes, but also is lower 1.19% in patching rate of hole than DACHP. In addition, DACHP is more stable in utilization of redundant nodes and does not produce additional cost.

关键词： wireless sensor networks coverage holes patching holes nodes utiliza

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Data Classification on Multiple Manifolds

Data Classification on Multiple Manifolds

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International Conference on pattern recognition

作者： Rui Xiao Qijun Zhao David Zhang Pengfei Shi Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University China Department of Computer Science Hong Kong Polytechnic University Hong Kong China

ISBN: (纸本)9781424475421

Unlike most previous manifold-based data classification algorithms assume that all the data points are on a single manifold, we expect that data from different classes may reside on different manifolds of possible different dimensions. Therefore, better classification accuracy would be achieved by modeling the data by multiple manifolds each corresponding to a class. To this end, a general framework for data classification on multiple manifolds is presented. The manifolds are firstly learned for each class separately, and a stochastic optimization algorithm is then employed to get the near optimal dimensionality of each manifold from the classification viewpoint. Then, classification is performed under a newly defined minimum reconstruction error based classifier. Our method could be easily extended by involving various manifold learning methods and searching strategies. Experiments on both synthetic data and databases of facial expression images show the effectiveness of the proposed multiple manifold based approach.

关键词： Manifolds Classification algorithms Accuracy Databases Training Tuning image reconstruction

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A Services Selection Policy for ServiceBSP Model with QoS-aware in Grids

A Services Selection Policy for ServiceBSP Model with QoS-aw...

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International Conference on Convergence Information Technology

作者： Yan Jiang Weiqing Tong Wentao Zhao School of Computer Engineering and Science Shanghai University China Image Processing and Pattern Recognition Institute Shanghai Jiaotong University Shanghai China

ISBN: (纸本)9781424431779

ServiceBSP model is presented as an extension of BSP model with a view to the advantages of BSP model in grid environment where large-scale and geographically distributed resources (abstracted as services) are available. In this paper, we advocate a preferable service broker framework to support the services selection policy that could optimize the services selection for ServiceBSP model. It means that the services selection can increase the level of satisfaction measured by overall quality value and decrease execution time by considering services dependent relation and location in a subtask group. Meanwhile, the policy is preliminary work ensuring reasonable distribution of services and loading balance in ServiceBSP model. In an addition, we give a detailed process of services selection based on heuristic greedy algorithm. Finally, by comparing response time and price using proposed policy with other policy only considering QoS, the experimental simulation shows that our services selection policy possesses superior performance on response time and then provides satisfactory result for users.

关键词： Quality of service Ontologies Large-scale systems Delay Information technology Grid computing pattern recognition Costs Time measurement Greedy algorithms

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Convergence of estimation of distribution algorithms in optimization of additively noisy fitness functions

Convergence of estimation of distribution algorithms in opti...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Yi Hong Qingsheng Ren Jin Zeng Yuchou Chang Department of Computer Science and Engineering Shanghai Jiaotong University China Department of Mathematics Shanghai Jiaotong University China Institute of Image Processing and Pattern Recognition China

Noise is a common phenomenon in many real-world optimizations. It has long been argued that evolutionary algorithm (EA) should be relatively robust against it. As a novel computing model in evolutionary computations, estimation of distribution algorithm (EDA) is also encountered with it. This paper initially presents three dynamic models of EDA under the additively noisy environment with three different selection methods (proportional selection method, truncation selection method and tournament selection method). We verify that when the population size is infinite, EDA can converge to the global optimal point. This concept establishes the theoretic foundation for optimization of noisy fitness functions with EDA

关键词： Convergence Electronic design automation and methodology Mathematics Genetic algorithms Genetic mutations Evolutionary computation Distributed computing Working environment noise Mathematical model computer science

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Cross-media analysis and reasoning: advances and directions

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Frontiers of Information Technology & Electronic Engineering 2017年第1期18卷 44-57页

作者： Yu-xin PENG Wen-wu ZHU Yao ZHAO Chang-sheng XU Qing-ming HUANG Han-qing LU Qing-hua ZHENG Tie-jun HUANG Wen GAO Institute of Computer Science and Technology Peking University Department of Computer Science and Technology Tsinghua University Institute of Information Science Beijing Jiaotong University National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of Sciences Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Sciences Department of Computer Science and Technology Xi'an Jiaotong University School of Electronics Engineering and Computer Science Peking University

Cross-media analysis and reasoning is an active research area in computer science, and a promising direction for artificial intelligence. However, to the best of our knowledge, no existing work has summarized the state-of-the-art methods for cross-media analysis and reasoning or presented advances, challenges, and future directions for the field. To address these issues, we provide an overview as follows：（1） theory and model for cross-media uniform representation; （2） cross-media correlation understanding and deep mining; （3） cross-media knowledge graph construction and learning methodologies; （4） cross-media knowledge evolution and reasoning; （5） cross-media description and generation; （6） cross-media intelligent engines; and （7） cross-media intelligent applications. By presenting approaches, advances, and future directions in cross-media analysis and reasoning, our goal is not only to draw more attention to the state-of-the-art advances in the field, but also to provide technical insights by discussing the challenges and research directions in these areas.

关键词： Cross-media analysis Cross-media reasoning Cross-media applications

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Normalized Linear Transform for Cross-Platform Microarray Data Integration

Normalized Linear Transform for Cross-Platform Microarray Da...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Huilin Xiong Ya Zhang Xue-wen Chen Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China Department of Electrical Engineering and Computer Science University of Kansas Lawrence KS USA

With microarray data being dramatically accumulated, integrating data from related studies represents a natural way to increase sample size so that more reliable statistical analysis may be performed. However, inherent variation among different microarray platforms makes the data integration not a trivial task. In this paper, we present a simple and effective integration scheme, called normalized linear transform (NLT), to combine data from different microarray platforms. The NLT scheme is compared with three other integration schemes for two tasks: classification analysis and gene marker selection. Our experiments demonstrate that the NLT scheme performs best in terms of classification accuracy under various classification settings, and leads to more biologically significant marker genes.

关键词： Costs Gene expression Support vector machines Support vector machine classification Machine learning Application software image processing pattern recognition computer science Statistical analysis

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SVM based ASM for facial landmarks location

SVM based ASM for facial landmarks location

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8th IEEE International Conference on computer and Information Technology (CIT 2008), vol.1

作者： Chunhua Du Qiang Wu Jie Yang Zheng Wu Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China Department of Computer Systems University of Technology Sydney Australia

Finding a new position for each landmark is a crucial step in active shape model (ASM). Mahalanobis distance minimization is used for this finding, provided there are enough training data such that the grey-level profiles for each landmark follow a multivariate Gaussian distribution. However, this condition could not be satisfied in most cases. In this paper, a new method support vector machine (SVM) based ASM (SVMBASM) is proposed. It approaches the finding task as a small sample size classification problem, and uses SVM classifier to deal with this problem. Moreover, considering imbalanced dataset which contains more negative instances (incorrect candidates for new position) than positive instances (correct candidates for new position), a multi-class classification framework is adopted. Performance evaluation on SJTU face database show that the proposed SVMBASM outperforms the original ASM in terms of the average error as well as the average frequency of convergence.

关键词： Active shape model (ASM) Support vector machine (SVM) Multi-class classification Facial landmark New position

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NCEM: Network structural similarity metric-based clustering for noisy cryo-EM single particle images

NCEM: Network structural similarity metric-based clustering ...

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Chinese Automation Congress

作者： Yin Shuo Biao Zhang Hong-Bin Shen Yang Yang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Department of Computer Science Shanghai Jiao Tong University Shanghai China

Cryo-EM single particle image reconstruction is currently a powerful technique for revealing the structure of biomacromolecules. Compared to traditional structural biology techniques like X-Ray, it requires fewer restrictions on specimens and is highly efficient with image processing tools. In this single particle reconstruction protocol, the ultimate goal is to identify different particle projection orientations. Since the picked single particle images are highly noisy, clustering is an important step to refrain noise by dividing images with similar projection angles into groups and averaging these images. The goal of clustering analysis is to assign similar particles into same class, so similarity measurement between particles is an important part in all clustering algorithms. Directly measuring the similarity of two particle images will be unreliable due to their low SNR. In this study, we propose a novel network structural similarity metric-based clustering algorithm NCEM for clustering the single particle images. We first construct a complex network for all particle images, where each node represents a particle. Then calculating the similarity between two nodes using structural similarity. This new network-based single particle image similarity metric has advantages over direct measurement for its noise resistance by using the structural information of the network. Our experiments on both artificial and experimental datasets demonstrate its effectiveness.

关键词： Cryo-EM Single particle image clustering Complex network Structural similarity

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From dense subgraph to graph matching: A label propagation approach

From dense subgraph to graph matching: A label propagation a...

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International Conference on Audio, Language and image processing, ICALIP

作者： Zhuoyi Zhao Yu Qiao Jie Yang Li Bai Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China School of Computer Science University of Nottingham Nottingham UK

ISBN: (纸本)9781479939046

Graph matching (GM) is a fundamental problem in computer science, and it has been successfully applied to provide solutions to many problems in computer vision. In this paper, we consider GM as a clustering problem in an association graph whose nodes represent candidate correspondences between two graphs to be matched. And we take the dense subgraph as a good prior for correct correspondences, thus we propose a label propagation approach to expand the dense subgraph to resolve the whole cluster. The label propagation approach is achieved by an affinity-preserving manifold ranking algorithm with a dynamic label vector which enforces the matching constraints. And the matching constraints is introduced through a doubly-stochastic normalization procedure. Extensive experiments demonstrate that our algorithm outperforms the state-of-the-art GM algorithms especially in the presence of outliers and deformation.

关键词： Manifolds Vectors Clustering algorithms Accuracy Heuristic algorithms Sparse matrices Noise

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The method of image restoration in the environments of dust

The method of image restoration in the environments of dust

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International Conference on Mechatronics and Automation

作者： Wang, Yuanyu Li, Yuanzong Zhang, Tianxu Department of Computer Fundamental Education TaiYuan University of Technology TaiYuan CO 030024 China College of Mechanical Engineering TaiYuan University of Technology TaiYuan CO 030024 China Institute for Pattern Recognition and A.I. State Key Laboratory of Image Processing and Intelligent Control HuaZhong University of Science and Technology WuHan CO 430074 China

ISBN: (纸本)9781424451418

In order to solve the problem of image degradation caused by dust environments, an image degradation model considering multiple scattering factors caused by dust was first established using the first-order multiple scattering method. Then, a dark channel prior principle was applied to present an image restoration algorithm based on the image degradation model. Finally, a particle swarm optimization algorithm was applied to optimize the atmospheric light and the exposure parameters. This optimization algorithm was established according to the criterion of the image evaluation based on kirsch operator with dual threshold. By using the method an optimistic result of image restoration was obtained. The experimental results have shown that the method not only enhanced luminance and contrast, but also discovered more detail edges information. The method provided a foundation for target recognition in the dust environments. © 2010 IEEE.

关键词： Dust

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