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International Conference on information, Communications and Signal Processing

作者： Jie Chen Ling-Yu Duan Bing Li Rongrong Ji Wen Gao Institute of Digital Media School of Electronic Engineering and Computer Science Peking University Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781457700293

We propose a novel locality sensitive vocabulary coding scheme to extract compact descriptors for low bit rate visual search. We employ Latent Dirichlet Allocation (LDA) to learn the topic vocabularies of lower dimension to generate compact descriptors. To deal with diverse datasets, LDA model is introduced to subdivide a dataset into groups of images with a topic model, where the code word distributions in each group produce coherent statistics from generative learning. Moreover, our empirical study has shown that the original Bag-of-Word (BoW) is sparse, and the occurrences of non-zero words is coherent within a topic. Our proposed topic-wise vocabulary learning yields a more compact yet discriminative codebook to search images. Given a query image, multiple topics are determined, which is fed into the topic vocabularies to generate more compact topical descriptor. Comparison experiments show our topic-wise locality sensitive vocabulary coding produces more compact and discriminative descriptors than the state-of-the-arts.

关键词： Vocabulary Visualization Encoding Mobile communication Image coding Histograms Bit rate

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Stereoscopic learning for disparity estimation

Stereoscopic learning for disparity estimation

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Zhebin Zhang Yizhou Wang Tingting Jiang Wen Gao National Engineering Laboratory for Video Technology Peking University Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Key Laboratory of Machine Perception (MoE) School of EECS Peking University Beijing China Peking University Beijing Beijing CN

In this paper, we propose a learning based approach to estimating pixel disparities from the motion information extracted out of input monoscopic video sequences. We represent each video frame with superpixels, and extract the motion features from the superpixels and the frame boundary. These motion features account for the motion pattern of the superpixel as well as camera motion. In the learning phase, given a pair of stereoscopic video sequences, we employ a state-of-the-art stereo matching method to compute the disparity map of each frame as ground truth. Then a multi-label SVM is trained from the estimated disparities and the corresponding motion features. In the testing phase, we use the learned SVM to predict the disparity for each superpixel in a monoscopic video sequence. Experiment results show that the proposed method achieves low error rate in disparity estimation.

关键词： Pixel Three dimensional displays Cameras Feature extraction Histograms Support vector machines Optical imaging

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Roughness in vector spaces

Roughness in vector spaces

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IEEE International Conference on Granular computing (GRC)

作者： Mingfen Wu Xiangyun Xie School of Computer Science Wuyi University Guangdong China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Mathematics and Computational Science Wuyi University China

Rough set theory proposed by Pawlak, is a complementary generalization of classical set theory. The relations between rough sets and algebraic systems endowed with two binary operations such as rings, groups and semigroups have been already considered. Wu, Xie and Cao defined a pair of rough approximation operators based on a sub-space. In this paper we do the further study about the upper (lower)approximations. We propose concepts of W-upper(lower) rough subspaces in an approximation space and give some properties of the upper(lower) and rough subspaces. Some characterizations of the upper(lower) rough subspace are expressed with addition and intersection operations of subspaces. At the same time, some counterexamples are offered to illustrate the relevant results.

关键词： Approximation methods Rough sets Vectors Educational institutions Laboratories

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An improved neural architecture for gaze movement control in target searching

An improved neural architecture for gaze movement control in...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jun Miao Lijuan Duan Laiyun Qing Yuanhua Qiao Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences (CAS) Beijing China College of Computer Science and Technology Beijing University of Technology Beijing China School of Information Science and Engineering Graduate University of Chinese Academy of Sciences Beijing China College of Applied Science Beijing University of Technology Beijing China

This paper presents an improved neural architecture for gaze movement control in target searching. Compared with the four-layer neural structure proposed in [14], a new movement coding neuron layer is inserted between the third layer and the fourth layer in previous structure for finer gaze motion estimation and control. The disadvantage of the previous structure is that all the large responding neurons in the third layer were involved in gaze motion synthesis by transmitting weighted responses to the movement control neurons in the fourth layer. However, these large responding neurons may produce different groups of movement estimation. To discriminate and group these neurons' movement estimation in terms of grouped connection weights form them to the movement control neurons in the fourth layer is necessary. Adding a new neuron layer between the third layer and the fourth lay is the measure that we solve this problem. Comparing experiments on target locating showed that the new architecture made the significant improvement.

关键词： Neurons Visualization Image coding Encoding Context Feature extraction Equations

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computing importance of 2D contour parts by reconstructability

Computing importance of 2D contour parts by reconstructabili...

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International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Ge Guo Yizhou Wang Tingting Jiang Alan Yuille Wen Gao Graduate University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China National Engineering Laboratory for Video Technology Schl of EECS Peking University Beijing China Department of Statistics University of California Los Angeles CA USA

Part-based representation has become an essential representation for objects. Different parts of a shape have various importance to shape perception. Concerned by the contributions of local parts to the perception of global shapes, we introduce an importance measure of shape parts from the viewpoint of their “reconstructability”. Given one shape part as a local observation, we estimate the global object shapes through a proposed novel shape reconstruction method. The reconstruction quality is determined by part geometry variation and part uniqueness. Then a conditional entropy formulation is introduced to measure the part importance considering the reconstruction quality, part uniqueness and variation. Extensive experiments demonstrate the benefit of the proposed part importance to object detection applications. Moreover, psychological experiments are conducted to further support the consistency of our importance measure with human perception towards shape parts.

关键词： Shape Entropy Computational modeling Image reconstruction Object detection Shape measurement Psychology

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Determination of relative significance factor of impact categories based on fuzzy multiattribute decision-making method

Determination of relative significance factor of impact cate...

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Chinese Control and Decision Conference, CCDC

作者： Xinyang Deng Yajuan Zhang Bingyi Kang Jiyi Wu Xiaohong Sun Yong Deng School of Computer and Information Sciences Southwest University Chongqing China Hangzhou Key Laboratory of E-Business and Information Security Hangzhou Normal University Hangzhou Zhejiang China College of Food Science and Technology Shanghai Ocean University Shanghai China Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan University Xiangtan Hunan China School of Electronics & Information Technology Shanghai Jiaotong University Shanghai China Laboratory of National defense for Radioactive Waste and Environmental Security Southwest University of Science and Technology Mianyang China

A relative significance factor of an impact category is the external weight of the impact category and plays an important role in life cycle assessment(LCA). Many multiattribute decision-making (MADM) methods such as analytical hierarchy process(AHP), the rank-order centroid method and the fuzzy method are proposed to determine the relative significance factor of an impact category. However, in previous research, it is shown that, the AHP approach is difficult for experts rendering consistent judgment when the number of pairwise comparisons increases, the rank-order method has some insurmountable shortcomings, the key issue to use fuzzy set theory is its complexity. In order to address this problem, a new simple and efficient method is proposed to determine relative significance factor in a more efficient manner. An example to determine relative significance factor of impact categories is shown to illustrate the efficiency of the proposed method.

关键词： Decision making Educational institutions Complexity theory Pragmatics Uncertainty Fuzzy set theory Fuzzy sets

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一种基于模型共享的半监督多标签图像学习法

一种基于模型共享的半监督多标签图像学习法

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第十二届中国虚拟现实大会

作者： ZHANG Da-peng 张大鹏 WEN Jia 闻佳 LIU Xi 刘曦 Institute of Information Science and Engineering Yanshan University Qinhuangdao 066004 China Guan 燕山大学信息科学与工程学院秦皇岛 066004 桂林电子科技大学广西可信软件重点实验室桂林 541004 Institute of Information Science and Engineering Yanshan University Qinhuangdao 066004 China 燕山大学信息科学与工程学院秦皇岛 066004 The Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese 中国科学院计算技术研究所智能信息处理重点实验室北京 100190

提出一种快速且有效的半监督多标签学习方法：模型共享半监督推举。该方法能发现、共享并组合多个基模型，每个基模型是在某个标签上利用半监督支持向量机(S3VM)上学习的。通过使用模型共享，标签关联被显示地利用且对于每个标签来说只... 详细信息

提出一种快速且有效的半监督多标签学习方法：模型共享半监督推举。该方法能发现、共享并组合多个基模型，每个基模型是在某个标签上利用半监督支持向量机(S3VM)上学习的。通过使用模型共享，标签关联被显示地利用且对于每个标签来说只需要少量的基模型即可生成最后的决策结果。在Core15k和Mediamill数据集上评估方法，实验结果显示的方法与当前流行的监督和半监督多标签学习方法是可比的。

关键词：信息处理半监督多标签学习算法模型共享支持向量机

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Research on Auto-generation of Operation Order for Distribution Network

Research on Auto-generation of Operation Order for Distribut...

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The Second International Conference on High Performance Networking, computing,and Communication Systems（ICHCC 2011）

作者： Yanwei Zheng Qingdong Meng Xuejun Lv Zhiquan Feng School of Information Science and Engineering University of Jinan Shandong Provincial Key Laboratory of Network based Intelligent Computing University of Jinan Department of Production Technology Yanggu Power Supply Corporation Department of City Power Supply Linyi Power Supply Corporation

To meet the requirements of operation orders auto-generation, a topology information data structure is designedAbove this, several algorithms are proposed, which support to generate operation orders for the tasks of cell maintenance, cell transmission, full circuitry maintenance, range of circuitry maintenance, and switch load algorithmsThese algorithms can use in the distribution networkThe auto-generation only accords the topology information that distilled from the graphicIt is easily use for the maintain personnel.

关键词： Auto-generation,Operation order,Distribution network,topology information

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Assessment of genetically modified food security-first results

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Journal of information and Computational Science 2011年第12期8卷 2493-2500页

作者： Chai, Jie Wu, Jiyi Cong, Jiang Sun, Xiaohong Kang, Bingyi Deng, Yong School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai 200240 China Hangzhou Key Laboratory of E-Business and Information Security Hangzhou Normal University Zhejiang 310036 China College of Food Science and Technology Shanghai Ocean University Shanghai 201306 China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan 411105 China Key Subject Laboratory of National Defense for Radioactive Waste and Environmental Security Southwest University of Science and Technology Mianyang 621010 China College of Computer and Information Sciences Southwest University Chongqing 400715 China

With the development of Genetically Modified (GM) food, security issue is being paid increasing attention. In this paper, a comprehensive model taking account factors which influencing GM food security is proposed. Analytical Hierarchy Process (AHP) is used to derive the weight of each attribute in different levels according to their importance. Basic Probability Assignment (BPA) function is generated by the safety degree of each element, then we use BPA function of the Dempster-Shafer evidence to represent the inclination of the elements. The safety degree of GM food will be get when weight was used as the discount factor and each attribute data was combined based on discount combination rule. 1548-7741/Copyright © 2011 Binary information Press.

关键词： Safety factor

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A method for translating belief function models to probability models

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Journal of information and Computational Science 2011年第10期8卷 1817-1823页

作者： Xu, Peida Wu, Jiyi Li, Ya Cong, Jiang Sun, Xiaohong Kang, Bingyi Deng, Yong School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai 200240 China Hangzhou Key Laboratory of E-Business and Information Security Hangzhou Normal University Zhejiang 310036 China College of Food Science and Technology Shanghai Ocean University Shanghai 201306 China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan 411105 China Key Subject Laboratory of National defense for Radioactive Waste and Environmental Security Southwest University of Science and Technology Mianyang 621010 China College of Computer and Information Sciences Southwest University Chongqing 400715 China

One of the key issues in the applications of Dempster-Shafer evidence theory is how to make a decision based on Basic Probability Assignment (BPA). The conventional method is Pignistic Probability Transformation (PPT), which transforms BPA to probability distribution with equally assignment. To solve the existing problems of pignistic probability transformation, a new transformation method using an optimization algorithm based on the conflict coefficient k is proposed in this paper. The obtained probability has the minimum conflict coefficient with the original BPA. Numerical examples are used to illustrate the efficiency of the proposed method. © 2011 by Binary information Press.

关键词： Decision making

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