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MIPPR 2009 - Remote Sensing and GIS Data processing and Other Applications: 6th International Symposium on Multispectral Image processing and Pattern Recognition

作者： Liu, Xin Zhou, Chengping Ding, Mingyue Cai, Chao Institute for Pattern Recognition and Artificial Intelligence National Lab. for Multi-spectral Information Processing Technologies Huazhong University of Science and Technology Wuhan 430074 China College of Life Science and Technology Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780819478092

A new path planning method for UAV in static workspace is presented. The method can find a nearly optimal path in short time which satisfies the UAV kinematic constraints. The method makes use of the skeletons to construct the graph of the planning space considering the configuration of the obstacles and utilizes the graph to find a shortest collision-free path, and a novel technique is utilized to convert the free path into a feasible path. The method can be applied to different applications and easy to be implemented. Experimental results showed that the path planning can be done in a fraction of second on a contemporary workstation (2-3 seconds) under the condition of satisfying the kinematic constraints. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Motion planning

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Active Learning of Instance-Level Constraints for Semi-supervised Document Clustering

Active Learning of Instance-Level Constraints for Semi-super...

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Weizhong Zhao Qing He Huifang Ma Zhongzhi Shi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

This paper presents a framework that actively selects informative documents pairs for semi-supervised document clustering. The semi-supervised document clustering algorithm is a Constrained DBSCAN (Cons-DBSCAN), which incorporates instance-level constraints to guide the clustering process in DBSCAN. By obtaining user feedbacks, our proposed active learning algorithm can get informative instance level constraints to aid clustering process. Experimental results show that Cons-DBSCAN with the proposed active learning approach can provide an appealing clustering performance.

关键词： intelligent agent Clustering algorithms Feedback Machine learning Conferences information processing Computers Data mining Learning systems

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Organizing structured deep web sources with semantic inference

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Journal of Computational information Systems 2009年第3期5卷 1157-1165页

作者： Huang, Li Fang, Wei Chen, Hongpin Zhao, Pengpeng Cui, Zhiming Jiangsu Key Laboratory of Computer Information Processing Technology Soochow University Suzhou 215006 China Institute of Intelligent Information Processing and Application Soochow University Jiangsu Suzhou 215006 China

Deep Web sources classification is one of key steps in Large-scale data integration, and structured query interface of Deep Web serves as a valid approach for research on online databases organization by domains. This paper proposes a novel classification strategy on Deep Web sources. Decisive features and co-occurrence features could be obtained by semantic inference from visible features of query interface, which enrich semantic features in form. Integration of semantic inference and machine learning method effectively can further improve traditional classification performance on Deep Web sources. Experimental results on real web datasets show that our method is improved with higher classification accuracy. 1553-9105/ Copyright © 2009 Binary information Press.

关键词： Data integration

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Improving tree-to-tree translation with packed forests 09

Improving tree-to-tree translation with packed forests

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Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language processing of the AFNLP: Volume 2 - Volume 2

作者： Yang Liu Yajuan Lü Qun Liu Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781932432466

Current tree-to-tree models suffer from parsing errors as they usually use only 1-best parses for rule extraction and decoding. We instead propose a forest-based tree-to-tree model that uses packed forests. The model is based on a probabilistic synchronous tree substitution grammar (STSG), which can be learned from aligned forest pairs automatically. The decoder finds ways of decomposing trees in the source forest into elementary trees using the source projection of STSG while building target forest in parallel. Comparable to the state-of-the-art phrase-based system Moses, using packed forests in tree-to-tree translation results in a significant absolute improvement of 3.6 BLEU points over using 1-best trees.

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Image features optimizing for content-based image retrieval

Image features optimizing for content-based image retrieval

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IEEE International Conference on intelligent Computing and intelligent Systems (ICIS)

作者： Zhiping Shi Xi Liu Qing He Zhongzhi Shi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

ISBN: (纸本)9781424447374;9781424447541

Developing low-dimensional semantics-sensitive features is crucial for content-based image retrieval (CBIR). In this paper, we present a method called M2CLDA (merging 2-class linear discriminant analysis) to capture low-dimensional optimal discriminative features in the projection space. M2CLDA calculates discriminant vectors with respect to each class in the one-vs-all classification scenario and then merges all the discriminant vectors to form a projection matrix. The dimensionality of the M2CLDA space fits in with the number of classes involved. Moreover, when a new class is added, the new M2CLDA space can be approximated by only calculating a new discriminant vector for the new class. The features in the M2CLDA space have better semantic discrimination than those in traditional LDA space. Our experiments show that the proposed approach improves the performance of image retrieval and image classification dramatically.

关键词： Image retrieval Content based retrieval Linear discriminant analysis information retrieval Scattering Vectors Image classification Data mining Principal component analysis Helium

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Joint decoding with multiple translation models 09

Joint decoding with multiple translation models

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Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language processing of the AFNLP: Volume 2 - Volume 2

作者： Yang Liu Haitao Mi Yang Feng Qun Liu Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781932432466

Current SMT systems usually decode with single translation models and cannot benefit from the strengths of other models in decoding phase. We instead propose joint decoding, a method that combines multiple translation models in one decoder. Our joint decoder draws connections among multiple models by integrating the translation hypergraphs they produce individually. Therefore, one model can share translations and even derivations with other models. Comparable to the state-of-the-art system combination technique, joint decoding achieves an absolute improvement of 1.5 BLEU points over individual decoding.

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Biological-inspired computational modeling of eye-motion control for object detection

Biological-inspired computational modeling of eye-motion con...

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Asian Control Conference

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

ISBN: (纸本)9781424454402

Eye movement plays an important role in human vision system. How to control eye or gaze movement automatically for image understanding is an interesting issue. This paper presents a progress of our research on biological-inspired computational modeling of eye-motion control for object detection in images. The model simulates the single and population cell coding mechanisms for learning visual context and controlling the eye movement. A comparative experiment with three coding systems is carried out and experimental results show the gradual-scale population coding system performs better than the other two coding systems on the average for object detection.

关键词： Computational modeling Biological control systems Object detection Humans Automatic control Biological system modeling Biological information theory Image coding Context modeling Feature extraction

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Identifying query interfaces of deep web entries automatically

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Journal of Computational information Systems 2009年第3期5卷 1477-1485页

作者： Cui, Zhiming Fang, Wei Huang, Li Xian, Xuefeng Zhang, Shukui Jiangsu Provincial Key Laboratory of Computer Information Processing Technology Soochow University Suzhou 215006 China Institute of Intelligent Information Processing and Application Soochow University Suzhou 215006 China

A large number of high-quality web information is deeply hidden in the Web, which can not be indexed by conventional search engines, be called Deep Web. Because query interface is the only entrance to the Deep Web, we should find Deep Web query entries firstly. In this paper, we propose a new strategy that automatically and accurately identifies Deep Web entries based on Maximum Entropy and Bayes classifier from features that extracted from web form, link and page. Our experiments over real web pages show that the new approach can classify Deep Web entries accurately and efficiently. 1553-9105/ Copyright © 2009 Binary information Press.

关键词： Search engines

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Automatically Organize Web Text Resources with Frequent Term Tree

Automatically Organize Web Text Resources with Frequent Term...

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International Conference on Computer and information technology (CIT)

作者： Xiaofeng Wang Zhongzhi Shi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Graduate University of the Chinese Academy of Sciences Chinese Academy and Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

With the expansion of the Web, automatically organizing large scale text resources, e.g. Web pages, becomes very important. Many Web sites, like Google and Yahoo, use hierarchical classification trees to organize text resources in Web. User can easily find the text resources that meet their requirements by navigating these hierarchical classification trees. Typically, the text resources in Web are manually assigned to the nodes of the hierarchical classification tree. This limits the hierarchical classification tree to organize large scale text resources. In this paper, we propose a Frequent Term Tree to improve the ability of hierarchical classification tree in organizing large scale text resources in Web. Different from the Fp-tree which is utilized to efficiently discover frequent patterns, the frequent term tree is used to organize resources with frequent pattern based classification. The frequent term tree can accurately assign text resources to each node of classification tree and improve the ability in organizing resources with the incremental classified text resources. The evaluation of the frequent term tree demonstrates that frequent term tree can effectively and efficiently organize text resources.

关键词： Classification tree analysis Large-scale systems Organizing Navigation Humans information processing Computers information technology Web pages Web sites

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Inductive transfer learning for unlabeled target-domain via hybrid regularization

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Chinese Science Bulletin 2009年第11期54卷 2470-2478页

作者： ZHUANG FuZhen LUO Ping HE Qing SHI ZhongZhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Hewlett Packard Labs China Beijing 100084 China Graduate University of Chinese Academy of Sciences Beijing 100190 China

Recent years have witnessed an increasing interest in transfer learning. This paper deals with the classification problem that the target-domain with a different distribution from the source-domain is totally unlabeled, and aims to build an inductive model for unseen data. Firstly, we analyze the problem of class ratio drift in the previous work of transductive transfer learning, and propose to use a normalization method to move towards the desired class ratio. Furthermore, we develop a hybrid regularization framework for inductive transfer learning. It considers three factors, including the distribution geometry of the target-domain by manifold regularization, the entropy value of prediction probability by entropy regularization, and the class prior by expectation regularization. This framework is used to adapt the inductive model learnt from the source-domain to the target-domain. Finally, the experiments on the real-world text data show the effectiveness of our inductive method of transfer learning. Meanwhile, it can handle unseen test points.

关键词：归纳学习正规化标签杂交归一化法预测概率文字资料归纳法

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