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2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009

作者： Li, Hong Hu, Xuegang Key Laboratory of Network and Intelligent Information Processing Department of Computer Science and Technology Hefei University Hefei China School of Computer and Information Hefei University of Technology Hefei China

ISBN: (纸本)9781424445073

Strong Negative Association Rules can reveal irrelevances hidden between frequent itemsets. Existing research has made significant efforts in discovering both positive and negative association rules from single database. This paper presents an efficient method for mining strong negative association rules in multi-database. The method produces some strong negative relational patterns (a kind of infrequent itemsets) by pruning and scanning constructed Multi-Database Frequent Pattern tree, and extracts strong negative association rules according to the proposed correlation model. The experimental results show the effectiveness and efficiency of the proposed algorithm. ©2009 IEEE.

关键词： Trees (mathematics)

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A new contour corner detector based on curvature scale space

A new contour corner detector based on curvature scale space

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6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009

作者： Sun, Junding Zhang, Zhaosheng School of Computer Science and Technology Henan Polytechnic University Jiaozuo 454003 China Provincial Key Laboratory of Computer Information Processing Technology Suzhou University 215006 China

ISBN: (纸本)9780769537351

Corner detection is a main concern in many computer vision applications like object recognition or image matching. Furthermore, detection is usually performed over the contour of the objects. This paper presents a novel algorithm to detect corners based on CSS (curvature scale space). A multi-scale curvature polynomial is defined as the sum or product of the curvature under all scales of the contour. The new method can not only enhance curvature extreme peaks effectively, but also suppress noise and trivial details and prevent smoothing some corners with the augment of the scale. On the other hand, the detected corners belonging to the concave or convex can be judged by the result sign of the curvature polynomial. Experiment results show that the new method is more effective in corner detection than the other algorithms mentioned in the paper. © 2009 IEEE.

关键词： Object recognition

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Contour-Shape recognition and retrieval based on chain code

Contour-Shape recognition and retrieval based on chain code

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2009 International Conference on Computational Intelligence and Security, CIS 2009

作者： Sun, Junding Xu, Heli School of Computer Science and Technology Henan Polytechnic University Jiaozuo 454003 China Provincial Key Laboratory of Computer Information Processing Technology Suzhou University Suzhou 215006 China

ISBN: (纸本)9780769539317

The direction chain code has been widely used in image coding and recognition for its simplicity and low storage requirement. However, the researches on how to use chain code for shape retrieval are few. The traditional retrieval methods, chain code histogram (CCH) and Minimize Sum Statistical Direction Code (MSSDC) did not take the distribution feature into consideration. In our previous work, three novel descriptors, chain code distribution vector (CCDV), chain code relativity entropy (CCRE), chain code spatial distribution entropy (CCSDE) are discussed which take full advantage of the statistical feature and the distribution, spatial and relativity feature. In this paper, we compared the five algorithms on a large set of shape database. The experimental results denote the efficiency of each method. © 2009 IEEE.

关键词： Entropy

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An effective texture spectrum descriptor

An effective texture spectrum descriptor

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5th International Conference on information Assurance and Security, IAS 2009

作者： Xiaosheng, Wu Junding, Sun School of Computer Science and Technology Henan Polytechnic University Jiaozuo 454003 China Provincial Key Laboratory of Computer Information Processing Technology Suzhou University Suzhou 215006 China

ISBN: (纸本)9780769537443

The center-symmetric local binary pattern (CS-LBP) is an effective extension to local binary pattern (LBP) operator. However, it discards some important texture information because of the ignorance of the center pixel and is hard to choose a threshold for recognizing the flat area. A novel improved CS-LBP operator, named ICS-LBP, is proposed in this paper. The new operator classifies the local pattern based on the relativity of the center pixel and the center-symmetric pixels instead of the gray value differences between the center-symmetric pixels as CS-LBP, which can fully extract the texture information discarded by CS-LBP descriptor. Comparisons are given among the three methods and the experimental results show the performance improvement of the new descriptor. © 2009 IEEE.

关键词： Local binary pattern

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Applying fuzzy DLs in the extraction of image semantics

Applying fuzzy DLs in the extraction of image semantics

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作者： Dasiopoulou, Stamatia Kompatsiaris, Ioannis Strintzis, Michael G. Centre for Research and Technology Hellas Informatics and Telematics Institute Thessaloniki Greece Information Processing Laboratory Electrical and Computer Engineering Department Aristotle University of Thessaloniki Greece

ISBN: (纸本)3642105610

Statistical learning approaches, bounded mainly to knowledge related to perceptual manifestations of semantics, fall short to adequately utilise the meaning and logical connotations pertaining to the extracted image semantics. Instigated by the Semantic Web, ontologies have appealed to a significant share of synergistic approaches towards the combined use of statistical learning and explicit semantics. While the relevant literature tends to disregard the uncertainty involved, and treats the extracted image descriptions as coherent, two valued propositions, this paper explores reasoning under uncertainty towards a more accurate and pragmatic handling of the underlying semantics. Using fuzzy DLs, the proposed reasoning framework captures the vagueness of the extracted image descriptions and accomplishes their semantic interpretation, while resolving inconsistencies rising from contradictory descriptions. To evaluate the proposed reasoning framework, an experimental implementation using the fuzzyDL Description Logic reasoner has been carried out. Experiments in the domain of outdoor images illustrate the added value, while outlining challenges to be further addressed. © 2009 Springer-Verlag.

关键词： Data description

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Detection of front-view vehicle with occlusions using AdaBoost

Detection of front-view vehicle with occlusions using AdaBoo...

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2009 International Conference on information Engineering and computer Science, ICIECS 2009

作者： Wu, Chunpeng Duan, Lijuan Miao, Jun Fang, Faming Wang, Xuebin College of Computer Sciences Beijing University of Technology Beijing 100124 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology China Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781424449941

In this paper, we propose a vehicle detection method based on AdaBoost. We focus on the detection of front-view car and bus with occlusions on highway. Samples with different occlusion situations are selected into the training set. By using basic and rotated Haar-like features extracted from the samples in the set, we train an AdaBoost-based cascade vehicle detector. The performance tests on static images and short time videos show that (1)our approach detects cars more effectively than buses (2)the real-time detection of our method on video proceeds at 30 frames per second. ©2009 IEEE.

关键词： Adaptive boosting

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Combining different visual vocabularies with different sizes for image categorization

Combining different visual vocabularies with different sizes...

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2009 2nd Asia-Pacific Conference on Computational Intelligence and Industrial Applications, PACIIA 2009

作者： Luo, Hui-Lan Wei, Hui Ren, Yuan Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai 200433 China School of Information Engineering Jiangxi University of Science and Technology Ganzhou Jiangxi Province China

ISBN: (纸本)9781424446070

In this paper, the advantages of ensemble methods are applied to image categorization. A novel method is introduced for image categorization by combining various visual vocabularies with different sizes in the popular vocabulary approach. The vocabulary approach describes an image as a bag of discrete visual codewords, where the frequency distributions of these words are used for image categorization. Based on vocabularies of various sizes, a classifier ensemble is learned, which can jointly exploit different information with various granularities. High classification accuracies of the proposed algorithm are demonstrated on four different datasets. ©2009 IEEE.

关键词： Classification (of information)

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Fluency, Adequacy, or HTER? Exploring Different Human Judgments with a Tunable MT Metric 4

Fluency, Adequacy, or HTER? Exploring Different Human Judgme...

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4th Workshop on Statistical Machine Translation, WMT 2009, immediately preceding the 12th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2009

作者： Snover, Matthew Madnani, Nitin Dorr, Bonnie J. Schwartz, Richard Laboratory For Computational Linguistics And Information Processing Institute For Advanced Computer Studies University of Maryland College Park United States Human Language Technology Center of Excellence Bbn Technologies

Automatic Machine Translation (MT) evaluation metrics have traditionally been evaluated by the correlation of the scores they assign to MT output with human judgments of translation performance. Different types of human judgments, such as Fluency, Adequacy, and HTER, measure varying aspects of MT performance that can be captured by automatic MT metrics. We explore these differences through the use of a new tunable MT metric: TER-Plus, which extends the Translation Edit Rate evaluation metric with tunable parameters and the incorporation of morphology, synonymy and paraphrases. TER-Plus was shown to be one of the top metrics in NIST's Metrics MATR 2008 Challenge, having the highest average rank in terms of Pearson and Spearman correlation. Optimizing TER-Plus to different types of human judgments yields significantly improved correlations and meaningful changes in the weight of different types of edits, demonstrating significant differences between the types of human judgments. ©2009 Association for Computational Linguistics.

关键词： Machine translation

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Semi-supervised discriminant analysis via spectral transduction

Semi-supervised discriminant analysis via spectral transduct...

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2009 20th British Machine Vision Conference, BMVC 2009

作者： Zhai, Deming Chang, Hong Li, Bo Shan, Shiguang Chen, Xilin Gao, Wen School of Computer Science and Technology Harbin Institute of Technology Harbin China Key Laboratory of Intelligent Information Processing Chinese Academy of Sciences Beijing China Institute of Digital Media Peking University Beijing China

ISBN: (纸本)1901725391

Linear Discriminant Analysis (LDA) is a popular method for dimensionality reduction and classification. In real-world applications when there is no sufficient labeled data, LDA suffers from serious performance drop or even fails to work. In this paper, we propose a novel method called Spectral Transduction Semi-Supervised Discriminant Analysis (STSDA), which can alleviate such problem by utilizing both labeled and unla-beled data. Our method takes into consideration both label augmenting and local structure preserving. First, we formulate label transduction with labeled and unlabeled data as a constrained convex optimization problem and solve it efficiently with a closed-form solution by using orthogonal projector matrices. Then, unlabeled data with reliable class estimations are selected with a balanced strategy to augment the original labeled data set. At last, LDA with manifold regularization is performed. Experimental results on face recognition demonstrate the effectiveness of our proposed method. © 2009. The copyright of this document resides with its authors.

关键词： Convex optimization

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Parallel sentences mining from the web

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Journal of Computational information Systems 2009年第6期5卷 1633-1641页

作者： Yan, Zhenxiang Feng, Yanhui Hong, Yu Yao, Jianmin Provincial Key Laboratory of Computer Information Processing Technology Soochow University Suzhou 215006 China

Parallel sentences can benefit many NLP applications (e.g., machine translation, cross language information retrieval.) In this paper, the candidate bilingual webs pages are returned by submit sentence pairs to search engine and then validated by surface patterns. We propose an algorithm to candidate bilingual resource extraction and filter useless bilingual web pages. The pair sentences included in the candidate bilingual web pages is verified by a maximum entropy classifier combining length, word-overlap, alignment and text location features. Training sets and the mining seeds are acquired automatically. Experiment shows satisfactory parallel resource mining performance. 1553-9105/ Copyright © 2009 Binary information Press.

关键词： Websites

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