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检索条件"机构=Laboratory of Intelligent Information Processing Institute of Computing Technology"

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Parzen discriminant analysis

Parzen discriminant analysis

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作者： Fang, Youhan Shan, Shiguang Chang, Hong Chen, Xilin Gao, Wen Beijing 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 this paper, we propose a non-parametric Discriminant Analysis method (no assumption on the distributions of classes), called Parzen Discriminant Analysis (PDA). Through a deep investigation on the non-parametric density estimation, we find that minimizing/maximizing the distances between each data sample and its nearby similar/dissimilar samples is equivalent to minimizing an upper bound of the Bayesian error rate. Based on this theoretical analysis, we define our criterion as maximizing the average local dissimilarity scatter with respect to a fixed average local similarity scatter. All local scatters are calculated in fixed size local regions, resembling the idea of Parzen estimation. Experiments in UCI machine learning database show that our method impressively outperforms other related neighbor based non-parametric methods. © 2008 IEEE.

关键词： Discriminant analysis

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A Hierarchical Scheme for Rapid Video Copy Detection

A Hierarchical Scheme for Rapid Video Copy Detection

引用

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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Acquiring Commonsense Knowledge about Properties of Concepts from Text

Acquiring Commonsense Knowledge about Properties of Concepts...

引用

International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Ya-nan Cao Cungen Cao Liangjun Zang Yao Zhu Shi Wang Dongsheng Wang Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

Commonsense knowledge plays an important role in various areas such as natural language understanding, information retrieval, etc. This paper presents a method for acquiring commonsense knowledge about properties of concepts by analyzing how adjectives are used with nouns in everyday language. We firstly mine a large scale corpus for potential concept-property pairs using lexico-syntactic patterns and then filter erroneously acquired ones based on heuristic rules and statistical approaches. For each concept, we automatically select the commonsensical properties and evaluate their applicability. Finally, we generate commonsense knowledge represented with explicit fuzzy quantifiers. Experimental results demonstrate the effectiveness of our approach.

关键词： Natural languages information retrieval Fuzzy systems Laboratories information processing Computers Large-scale systems Filters Text recognition Statistical distributions

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Web video recommendation and long tail discovering

Web video recommendation and long tail discovering

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiao Wu Yongdong Zhang Junbo Guo Jintao Li Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

Given countless web videos available online, one problem is how to help users find videos to their taste in an efficient way. In this paper, to facilitate userpsilas browsing we propose relevant and exploratory recommendation algorithms utilizing multimodal similarity and contextual network to organize web videos of various topics. Comparison experiments demonstrate proposed approach generates more accurate video relevancy. And our method is more flexible in discovering user latent interests in long tail videos.

关键词： YouTube Correlation Visualization Partitioning algorithms Clustering algorithms Construction industry History

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Geodesic distance based aproach for sentence similarity computation

Geodesic distance based aproach for sentence similarity comp...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Hui-Fang Ma Qing He Zhong-Zhi Shi Chinese Academy of Sciences Beijing China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

This paper presents a novel approach based on geodesic distance for sentence similarity computation, which can be used in a query-based information retrieval system. Unlike the traditional distance methods, geodesic distance takes into account the spatial relationships of sentences, which better reflects the intrinsic geometric structure of sentence manifold. Experiments demonstrate that the proposed method shows a better correlation to human intuition compared with traditional Euclidean method.

关键词： Distance measurement Manifolds Level measurement Euclidean distance Machine learning Correlation Helium

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Measuring Taxonomic Similarity between Words Using Restrictive Context Matrices

Measuring Taxonomic Similarity between Words Using Restricti...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Shi Wang Cungen Cao Ya-nan Cao Han Lu Xinyu Cao Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

Measuring taxonomic similarity between words plays an important role in many semantic-based applications but still remains a challenging task today. We propose a new method which utilizes restrictive context matrices for this problem. We learn a set of special lexico-syntactic patterns automatically and use them to extract taxonomic related contexts of words from raw text. These restrictive contexts are then transformed into real matrices and similarities between them are calculated to reflect the taxonomic similarities between words. The main contribution of our work is that taxonomic related context of words can be mined, evaluated, and used to measure taxonomic similarities between words. Experimental results on Miller-Charles benchmark dataset achieve a correlation coefficient of 0.856.

关键词： Web search Thesauri Fuzzy systems Laboratories information processing information retrieval Natural language processing Ontologies Machine learning Robustness

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Spatio-temporal visual consistency for video copy detection

Spatio-temporal visual consistency for video copy detection

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2008 5th International Conference on Visual information Engineering (VIE 2008)

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

Video copy detection is essentially a problem of large scale pattern matching. Various copy attacks which change the visual appearance impose hazard on this task. Based on the spatio-temporal consistency, our algorithm aims to utilize the invariant pattern of visual information for video matching. Position correlation of trajectory feature points is calculated as the signature for fast detection. Experiments using benchmarked dataset and commonly happened copy attacks verify the robustness and efficiency of our algorithm.

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Object retrieval based on spatially frequent items with informative patches

Object retrieval based on spatially frequent items with info...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Ke Gao Shouxun Lin Junbo Guo Dongming Zhang Yongdong Zhang Yufeng Wu Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

Spatial relation of local image patches plays an important role in object-based image retrieval. An approach called spatial frequent items is proposed as an extension of Bag-of-Words method by introducing spatial relations between patches. Spatial frequent items are defined as frequent pairs of adjacent local image patches in polar coordinates, and exploited using data mining. Based on these frequent configurations, we develop a method to encode patches and their spatial relations for image indexing and retrieval. Besides, to avoid the interference of background patches, informative patches are filtrated based on their local entropy and self-similarity in the preprocess stage. Experimental results demonstrate that our method can be 8.6% more effective than the state-of-art object retrieval methods.

关键词： Entropy Visualization Filtration Data mining Distance measurement Image retrieval Detectors

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Extracting Part-Whole Relations from Unstructured Chinese Corpus

Extracting Part-Whole Relations from Unstructured Chinese Co...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Xinyu Cao Cungen Cao Shi Wang Han Lu Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

An important problem in text mining is the automatic extraction of semantic relations. The paper provides a domain independent method for automatic extraction of part-whole relations in Chinese corpusa. The method consists of there phases. First, a set of lexico-syntactical patterns for part-whole relations are designed using known pairs of concepts encoding part-whole relations as seeds, and manually filtering the extracted sentences. Second, Pairs of concepts are extracted using the patterns from a training corpus, which may reflect part-whole relations. Finally, the extracted pairs of concepts are further confirmed using a set of heuristic rules generated based on an analysis of Chinese syntactical and semantic features. Based on a test corpus, the method achieves satisfactory results.

关键词： Hard disks Zinc Fuzzy systems Data mining information processing Computers Filters Text mining Encoding Filtering

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