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Visual information retrieval from 2D shapes by bipolar-matching

Visual information retrieval from 2D shapes by bipolar-match...

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IEEE International Conference on information Reuse and Integration (IRI)

作者： Yuqing Song Institute of Computing Technology Key Laboratory of Intelligent Information Processing Chinese Academy and Sciences China

One of the most challenging issues in visual information retrieval is retrieval by shape, due to a lack of mathematically rigorous definition of shape similarity. This paper presents a bipolar model for computing shape similarity. Given a discrete region, we cut its Voronoi diagram into two parts along the border of the region and each part is a tree. We use the two trees to respectively model the structures of a region and its complement, which is called the Bipolar Model. We prune the two trees by removing the nodes with small protrusions. The leaf nodes of the pruned trees are interleaved to make a leaf chain. Two regions are compared and matched, using a cyclic edit distance between the two leaf chains, with restricted merge and split operations allowed. We tested our algorithm on the MPEG-7 data set and made a “bullseye” score of 89.9%, which is the best performance ever reported.

关键词： Shape Electric shock Transform coding Computational modeling Nearest neighbor searches Classification algorithms Visualization

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A level set based method of license plate localization

A level set based method of license plate localization

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IEEE International Conference on information Reuse and Integration (IRI)

作者： Yuqing Song Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences China

This paper presents a robust and real time method of license plate localization based on level sets. The proposed algorithm consists of three steps: (1) medial axis transformation of selected level sets, (2) identification of sub-strokes, and (3) a multi-resolution window-based analysis for plate localization. Experiments show the effectiveness of our algorithm.

关键词： Level set Licenses Algorithm design and analysis Accuracy Skeleton Image segmentation Pixel

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Object tracking by chiaroscuro patterns

Object tracking by chiaroscuro patterns

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IEEE International Conference on information Reuse and Integration (IRI)

作者： Yuqing Song Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences China

Chiaroscuro in art is characterized by strong contrasts between light and dark. An object in a certain light condition has a certain chiaroscuro pattern in appearance; and this pattern is invariant to the changes of illumination and pose within a certain extent. In this paper we introduce an object tracking method based on chiaroscuro patterns. The chiaroscuro pattern of an image is computed by running a medial axis transform on selected level sets. The medial axes are pruned and decomposed into segments. These segments make a chiaroscuro pattern. The end points of the segments are collected as key points, which are fed to Kalman filter for object tracking. The effectiveness of the algorithm is demonstrated by experiments.

关键词： Image segmentation Skeleton Kalman filters Level set Target tracking Transforms

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Translating Separation Logic into a Fragment of the First-Order Logic

Translating Separation Logic into a Fragment of the First-Or...

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Sixth International Conference on Semantics,Knowledge and Grids(第六届语义、知识与网格国际会议 SKG 2010)

作者： Cungen Cao Yuefei Sui Yuming Shen Ju Wang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Aca Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Aca School of Computer Science and Information Engineering Guangxi Normal University Guilin China 54100

Separation logic is an extension of Hoare logic for reasoning about mutable heap structure. To represent separation logic in the first-order logic, there are several choices to determine what are constants, what are predicates and quantifiers, and whether the commands are taken as atomic or composite. This paper shall give a translation of separation logic into a guarded fragment of the first-order logic, such that the translation is faithful, that is, the translation translates a consistent statement (boolean expression, assertion or specification) of separation logic into a consistent formula in the fragment of the first-order logic. By the decidability of the satisfiability problem of the guarded first-order logic, if the commands are taken as atomic in the first-order logic then the guarded first-order logic translated from separation logic is decidable;if the commands are taken as atomic/composite in the first-order logic then the first-order logic translated from separation logic is undecidable.

关键词： Semantics Syntactics Computers Cognition Object oriented modeling information processing Laboratories

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Predicting RNA secondary structure with Tabu Search

Predicting RNA secondary structure with Tabu Search

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IEEE International Conference on Cognitive Informatics

作者： Liu, Yongguo Hao, Jianrui Peng, Jun School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 611731 China Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Xiangtan University Xiangtan 411105 China College of Electronic Information Engineering Chongqing University of Science and Technology Chongqing 400050 China

ISBN: (纸本)9781424480401

In this paper, a novel method for predicting RNA secondary structure called RNA secondary structure prediction based on Tabu Search (RNATS) is proposed. In RNATS, two search models, intensification search and diversification search, are designed to exploit the local regions around the current solution and explore the unvisited space, respectively. Simulation experiments are conducted for six RNA sequences to show that the proposed method is feasible and effective. © 2010 IEEE.

关键词： Tabu search

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Exploiting associations between word clusters and document classes for cross-domain text categorization

Exploiting associations between word clusters and document c...

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10th SIAM International Conference on Data Mining, SDM 2010

作者： Zhuang, Fuzhen Luo, Ping Xiong, Hui He, Qing Xiong, Yuhong Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China Hewlett Packard Labs China MSIS Department Rutgers University United States Graduate University of Chinese Academy of Sciences China

Cross-domain text categorization targets on adapting the knowledge learnt from a labeled source-domain to an unla-beled target-domain, where the documents from the source and target domains are drawn from different distributions. However, in spite of the different distributions in raw word features, the associations between word clusters (conceptual features) and document classes may remain stable across different domains. In this paper, we exploit these unchanged associations as the bridge of knowledge transformation from the source domain to the target domain by the nonnegative matrix tri-factorization. Specifically, we formulate a joint optimization framework of the two matrix tri-factorizations for the source and target domain data respectively, in which the associations between word clusters and document classes are shared between them. Then, we give an iterative algorithm for this optimization and theoretically show its convergence. The comprehensive experiments show the effectiveness of this method. In particular, we show that the proposed method can deal with some difficult scenarios where baseline methods usually do not perform well. Copyright © by SIAM.

关键词： Iterative methods

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Conditional Random Fields for Machine Translation System Combination

Conditional Random Fields for Machine Translation System Com...

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International Conference on Asian Language Processing (IALP)

作者： Tian Xia Shandian Zhe Qun Liu Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

Minimum Error Rate Training (MERT) as an effective parameters learning algorithm is widely applied in machine translation and system combination area. However, there exists an ambiguity problem in respect to the training goal and it is hard for MERT to tackle, that is different parameters may lead to the same minimum error rate in training but greatly different performances in testing. We propose a novel training objective as the unique goal for training towards, namely partial references, and by use of conditional random fields (CRF) to cast the decoding procedure in system combination as a sequence labeling problem. Experiments on Chinese-English translation test sets show that our approach significantly outperforms the MERT-based baselines with less training time.

关键词： Training Helium Decoding Computational modeling Skeleton Context modeling Error analysis

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Doppler blood flow signal analysis meets traditional Chinese pulse diagnosis

Doppler blood flow signal analysis meets traditional Chinese...

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International Conference on BioMedical Engineering and Informatics (BMEI)

作者： Xiaorui Jiang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

In traditional Chinese pulse diagnosis (TCPD), diseases of internal organs can be detected by recognizing pulse waveform patterns of wrist radial arterial. However pulse waveform analysis, for which Doppler diagnosis is a powerful tool, is limited to cardiovascular diseases. This paper tries to fill the gap between TCPD and Doppler diagnosis by applying signal analysis and pattern recognition technologies to Doppler blood flow signals (DBFS's) of wrist radial arterial, which are recorded from both hands of healthy people, gastritis and cholecystitis patients. DBFS's are classified using the features proposed by an L2-soft margin support vector machine (L2-SVM): five clinical Doppler parameters (DP), wavelet energies (WE), wavelet packet energies (WPE), and piecewise axially integrated bispectra (PAIB). 5-fold cross validation is used for performance evaluation. The sick are differentiated from the healthy with an accuracy of about 80% using DP, WE and WPE, while the classification rate between gastritis and cholecystitis reaches 100%. Using PAIB, ether two groups of subjects are classified with accuracy greater than 93%. Gastritis is more accurately recognized than cholecystitis, while the latter is recognized with a higher accuracy on data from the left hand than right. Though the sample size is relatively small, we still argue that the methods proposed here are effective and could serve as an assisstive tool for TCPD.

关键词： Doppler effect Wavelet packets Feature extraction Diseases Wrist Pathology Accuracy

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Preservative translations between logical systems

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作者： Shen, Yuming Ma, Yue Cao, Cungen Sui, Yuefei Wang, Ju Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences 100190 China Graduate University of Chinese Academy of Sciences 100049 China School of Computer Science and Information Engineering Guangxi Normal University 541004 China

ISBN: (纸本)3642163262

This paper investigates a subclass of translations between logical systems, called the preservative translations, which preserve the satisfiability and the unsatisfiability of formulas. The definition of preservative translation is given and connections between the preservative translation and other definitions of translations in the literature are discussed. Some properties of logical systems, such as the compactness and the decidability, which are characterized by the existence of preservative translations between logical systems are also studied. © 2010 IFIP.

关键词： Artificial intelligence

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Collaborative dual-PLSA: Mining distinction and commonality across multiple domains for text classification 10

Collaborative dual-PLSA: Mining distinction and commonality ...

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Proceedings of the 19th ACM international conference on information and knowledge management

作者： Zhuang, Fuzhen Luo, Ping Shen, Zhiyong He, Qing Xiong, Yuhong Shi, Zhongzhi Xiong, Hui Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS China Graduate University of Chinese Academy of Sciences China Hewlett Packard Labs China China Innovation Works China MSIS Department Rutgers University United States

ISBN: (纸本)9781450300995

The distribution difference among multiple data domains has been considered for the cross-domain text classification problem. In this study, we show two new observations along this line. First, the data distribution difference may come from the fact that different domains use different key words to express the same concept. Second, the association between this conceptual feature and the document class may be stable across domains. These two issues are actually the distinction and commonality across data domains. Inspired by the above observations, we propose a generative statistical model, named Collaborative Dual-PLSA (CD-PLSA), to simultaneously capture both the domain distinction and commonality among multiple domains. Different from Probabilistic Latent Semantic Analysis (PLSA) with only one latent variable, the proposed model has two latent factors y and z, corresponding to word concept and document class respectively. The shared commonality intertwines with the distinctions over multiple domains, and is also used as the bridge for knowledge transformation. We exploit an Expectation Maximization (EM) algorithm to learn this model, and also propose its distributed version to handle the situation where the data domains are geographically separated from each other. Finally, we conduct extensive experiments over hundreds of classification tasks with multiple source domains and multiple target domains to validate the superiority of the proposed CD-PLSA model over existing state-of-the-art methods of supervised and transfer learning. In particular, we show that CD-PLSA is more tolerant of distribution differences. © 2010 ACM.

关键词： Classification (of information)

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