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New approach to heterogeneous semantic search on the web

Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2008年第8期45卷 1338-1345页

作者： Huang, Rui Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100049 China

Relevance ranking is a key to Web search in determining how results are retrieved and ordered. As keyword-based search does not guarantee relevance in meanings, semantic search has been put forward as an attractive and promising approach. Recently several kinds of semantic information have been adopted in search respectively, such as thesauruses, ontologies and semantic markups, as well as folksonomies and social annotations. However, although to integrate more semantics would logically generate better search results, search mechanism to fully adopt different kinds of semantic information is still in absence and to be researched. To these ends, an integrated semantic search mechanism is proposed to incorporate textual information and keyword search with heterogeneous semantic information and semantic search. A statistical based measurement of semantic relevance, defined as semantic probabilities, is introduced to integrate both keywords and four kinds of semantic information including thesauruses, categories, ontologies and folksonomies. It is calculated with all textual information and semantic information, and stored in a newly proposed index structure called semantic-keyword dual index. Based on this uniform measurement, the search mechanism is developed that fully utilizes existing keyword and semantic search mechanisms to enhance heterogeneous semantic search. Experiments show that the proposed approach can effectively integrate both keyword-based information and heterogeneous semantic information in search.

关键词： Semantic Web

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Survey of semantic mapping in image retrieval

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Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics 2008年第8期20卷 1085-1096页

作者： Li, Zhixin Shi, Zhiping Li, Zhiqing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100049 China

Semantic gap has become a bottleneck of content-based image retrieval. In order to bridge the gap and improve retrieval accuracy, a map from lower-level visual features to high-level semantics should be formulated. This paper provides a comprehensive survey on semantic mapping. Firstly, an image retrieval framework integrated with high-level semantics is presented. Secondly, image semantic description is introduced in two aspects: image content level-models and semantic representations. Thirdly, as the emphasis of this paper, semantic mapping approaches and techniques are investigated by classifying them into four main categories in terms of their characteristics. Various ideas and models proposed in these approaches are analyzed. In addition, advantages and limitations of each category are discussed. Finally, based on the state-of-the-art technology and the demand from real-world applications, several important issues related to semantic image retrieval are identified and some promising research directions are suggested.

关键词： Image annotation

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Point based large-scale crowds real-time 3D visualization method

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2008年第10期45卷 1731-1738页

作者： Shu, Bo Mao, Tianlu Xu, Wenbin Wang, Zhaoqi Virtual Reality Laboratory Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100049 China

3D visualization of large-scale virtual crowds is a very important and interesting problem in research fields of virtual reality. For reasons of efficiency and visual realism, it is very difficult to populate, animate and render large-scale virtual crowds with hundreds of thousand individually animated virtual characters in real-time applications;especially for the scene which has more than ten thousands of individuals with different shapes and motions. In this paper, an efficient method to visualize large-scale virtual crowds is presented. Firstly, using model variation technique, many different models can be derived from a small number of model templates. Secondly, the crowds can be animated individually by deforming a small number of elements in motion database. Thirdly, using a developed point sample rendering algorithm, large-scale crowds can be displayed in real-time. This method can be used to visualize different dynamic crowds which require both real-time efficiency and large number of virtual individuals support. Based on this work, an efficient and readily usable 3D visualization system is presented. It can provide very high visual realism for large crowds' visualization in interactive frame rates on a regular PC. The 3D visualization of 60000 people evacuating from a building is also realized in real-time based on the system.

关键词： Virtual reality

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Method of adaptively selecting best LDA model based on density

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Jisuanji Xuebao/Chinese Journal of Computers 2008年第10期31卷 1780-1787页

作者： Cao, Juan Zhang, Yong-Dong Li, Jin-Tao Tang, Sheng Virtual Reality Laboratory Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100049 China

Topic models have been successfully used to information classification and retrieval. These models can capture word correlations in a collection of textual documents with a low-dimensional set of multinomial distribution, called "topics". It is important but difficult to select an appropriate number of topics for a specific dataset. This paper proposes a theorem that the model reaches optimum as the average similarity among topics reaches minimum, and based on this theorem, proposes a method of adaptively selecting the best LDA model based on density. Experiments show that the proposed method can achieve performance matching the best of LDA without manually tuning the number of topics.

关键词： Classification (of information)

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Convergence property analysis for PSO based on cluster-degree

Convergence property analysis for PSO based on cluster-degre...

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2008 International Conference on Computational Intelligence and Security, CIS 2008

作者： Quansheng, Dou Zhijun, Yu Ping, Jiang Zhongzhi, Shi Yantai Dongfang Electronics Information Industry Group Co. Ltd. Yantai China School of Nursing Binzhou Medical University Yantai China School of Computer Science and Technology Shandong Institute of Business and Technology Yantai China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9780769535081

The concept of cluster-degree was put forward and distribute status of particle with different clusterdegree was studied. The reasonable parameters setting range based on cluster-degree was proposed. Under the direction of cluster-degree, many parameters which can get better searching results were found. So this paper is helpful for the choosing and adjustment of PSO parameters in practical application. © 2008 IEEE.

关键词： Convergence Computational intelligence Computer security information security information analysis Industrial electronics Computer industry Electronics industry Medical services Computer science

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Object-based image retrieval with attention analysis and spatial re-ranking

IFIP Advances in Information and Communication Technology

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IFIP Advances in information and Communication technology 2008年 288卷 118-128页

作者： Gao, Ke Lin, Shouxun Zhang, Yongdong Tang, Sheng Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Graduate University of the Chinese Academy of Sciences Beijing100080 China Key Laboratory of Intelligent Information Processing nstitute of Computing Technology Chinese Academy of Sciences Beijing100080 China

ISBN: (纸本)9780387876849

In this paper, a new method is proposed for object-based image retrieval. The user supplies a query object by selecting a region from a query image, and the system returns a ranked list of images that contain the same object, retrieved from a large image database. The main outcomes of this research are as follows: (1) An novel object-based image retrieval framework that integrates effective pre-treatment and re-ranking is presented, (2) a new feature filtration method based on attention analysis is proposed for pre-treatment, (3) to further improve object retrieval precision, we add an efficient spatial configuration model to rerank the primary retrieval result using Bag of Word method. Experimental results demonstrate the effectiveness of our method. © 2008, Springer New York LLC . All rights rserved.

关键词： Query processing

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Fast commercial detection based on audio retrieval

Fast commercial detection based on audio retrieval

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Dan Zhao Xiangdong Wang Yueliang Qian Qun Liu Shouxun Lin Institute of Computing Technology Key Laboratory of Intelligent Information Processing Beijing China Chinese Academy of Sciences Beijing China

Automatic detection of commercials in digital multimedia material is a challenging task with many applications. This paper presents a novel approach to fast commercial detection based on audio retrieval. It is based on the idea of segmenting energy envelope of audio into units, using only audio signal for matching on a commercial database. Fast searching and matching can be performed with high accuracy, by searching and by novel similarity function based on units. Experimental results show that 96.8% recall rate and 98.7% precision rate can be achieved under 0.125 real-time.

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A Hierarchical Scheme for Rapid Video Copy Detection

A Hierarchical Scheme for Rapid Video Copy Detection

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Xiao Wu Yongdong Zhang Sheng Tang Tian Xia Jintao Li Chinese Academy of Sciences China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

Today with the rapid increasing popularity of web video sharing, digital copyright protection encounters many troubles. Video copy detection schemes are emerging to cope with the digital video piracy and illegal distribution problems. But the large amount of video data and diversity of copy attacks pose difficulties on copy detection. This paper presents a hierarchical scheme to detect video copies, especially the temporal attacked and re-encoded ones. Our algorithm which is based on the ordinal signature of intra frames and effective R*-tree indexing structure archives real time performance. Comparison experiments are conducted on the benchmarked database of CIVR 2007 copy detection showcase and demonstrate the promising results of the proposed approach.

关键词： Video sharing Indexing Motion pictures Face detection Laboratories information processing Computers Copyright protection Data security Databases

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Multi-Agent based automatic evaluation system for classification algorithms

Multi-Agent based automatic evaluation system for classifica...

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International Conference on information and Automation (ICIA)

作者： Fuzhen Zhuang Qing He Zhongzhi Shi Chinese Academy of Sciences China China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences China

The current researches on classification usually focus on proposing novel algorithms and improving existent ones with better performances. However, although a classification algorithm is able to perform well for some given data sets, does it mean to any other data sets? And given several algorithm candidates, which one is the best for your classification problem? A practical solution to the above questions is to evaluate possible situations, which is extremely time-consuming and resource-consuming. In this paper we propose a distributed computing environment based on Multi-Agent technology to facilitate this evaluation process. In this computing environment we compare the performances of the same algorithm on different data sets, and different algorithms on the same data set. Experiments show that autonomic agents can run simultaneously and automatically on different computing hosts to achieve high availability, and this scheme can save the total evaluation time greatly. Therefore, this scheme will help us easily select the proper algorithm for a given classification problem according to different evaluation measures.

关键词： Classification algorithms Support vector machines Data mining Algorithm design and analysis Containers Training Testing

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Hallucinating facial images and features

Hallucinating facial images and features

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作者： Li, Bo Chang, Hong Shan, Shiguang Chen, Xilin Gao, Wen Harbin Institute of Technology Harbin China Digital Media Research Center Institute of Computing Tech. CAS Beijing China Key Laboratory of Intelligent Information Processing CAS Beijing China Digital Media Institute Peking University Beijing China

ISBN: (纸本)9781424421756

In facial image analysis, image resolution is an important factor which has great influence on the performance of face recognition systems. As for low-resolution face recognition problem, traditional methods usually carry out super-resolution firstly before passing the super-resolved image to a face recognition system. In this paper, we propose a new method which predicts high-resolution images and the corresponding features simultaneously. More specifically, we propose "feature hallucination" to project facial images with low-resolution into an expected feature space. As a result, the proposed method does not require super-resolution as an explicit preprocessing step. In addition, we explore a constrained hallucination that considers the local consistency in the image grid. In our method, we use the index of local visual primitives [5] as features and a block-based histogram distance to measure the similarity for the face recognition. Experimental results on FERET face database verify that the proposed method can improve both visual quality and recognition rate for low-resolution facial images. © 2008 IEEE.

关键词： Image resolution

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