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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： Hai Zhuge Knowledge Grid Research Group Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences 100190 Beijing China

Natural physical space provides material basis for the birth and evolution of human beings and *** progress of human society has created the cyber space. With the rapid development of information technology, the cyber space is connecting physical space, social space and mental space to form a new world - Cyber Physical Society. The way to explore the cyber physical society is different from the way to explore the natural physical space and society. This paper describes the ideal of the Cyber Physical Society, and presents its distinguished characteristics and scientific issues. Research on the Cyber Physical Society could lead to the revolution of society, science and technology.

关键词： Semantics Space technology Aerospace electronics Humans Materials Communities Sensors

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A Laplacian eigenmaps based semantic similarity measure between words

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作者： Wu, Yuming Cao, Cungen Wang, Shi Wang, Dongsheng Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences No.6 Kexueyuan South Road Zhongguancun Beijing 100-190 China Graduate University of Chinese Academy of Sciences No. 19 Yu Quan Road Shi Jing Shan Distinct Beijing 100-049 China

ISBN: (纸本)3642163262

The measurement of semantic similarity between words is very important in many applicaitons. In this paper, we propose a method based on Laplacian eigenmaps to measure semantic similarity between words. First, we attach semantic features to each word. Second, a similarity matrix ,which semantic features are encoded into, is calculated in the original high-dimensional space. Finally, with the aid of Laplacian eigenmaps, we recalculate the similarities in the target low-dimensional space. The experiment on the Miller-Charles benchmark shows that the similarity measurement in the low-dimensional space achieves a correlation coefficient of 0.812, in contrast with the correlation coefficient of 0.683 calculated in the high-dimensional space, implying a significant improvement of 18.9%. © 2010 IFIP.

关键词： Semantics

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D-LDA: A Topic Modeling Approach without Constraint Generation for Semi-defined Classification

D-LDA: A Topic Modeling Approach without Constraint Generati...

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IEEE International Conference on Data Mining (ICDM)

作者： Fuzhen Zhuang Ping Luo Zhiyong Shen Qing He Yuhong Xiong Zhongzhi Shi Chinese Academy and Sciences China Hewlett Packard Laboratories China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences China Innovation Works USA

We study what we call semi-defined classification, which deals with the categorization tasks where the taxonomy of the data is not well defined in advance. It is motivated by the real-world applications, where the unlabeled data may also come from some other unknown classes besides the known classes for the labeled data. Given the unlabeled data, our goal is to not only identify the instances belonging to the known classes, but also cluster the remaining data into other meaningful groups. It differs from traditional semi-supervised clustering in the sense that in semi-supervised clustering the supervision knowledge is too far from being representative of a target classification, while in semi-defined classification the labeled data may be enough to supervise the learning on the known classes. In this paper we propose the model of Double-latent-layered LDA (D-LDA for short) for this problem. Compared with LDA with only one latent variable y for word topics, D-LDA contains another latent variable z for (known and unknown) document classes. With this double latent layers consisting of y and z and the dependency between them, D-LDA directly injects the class labels into z to supervise the exploiting of word topics in y. Thus, the semi-supervised learning in D-LDA does not need the generation of pair wise constraints, which is required in most of the previous semi-supervised clustering approaches. We present the experimental results on ten different data sets for semi-defined classification. Our results are either comparable to (on one data sets), or significantly better (on the other nine data set) than the six compared methods, including the state-of-the-art semi-supervised clustering methods.

关键词： Data models Mathematical model Clustering algorithms Analytical models Companies Equations Web pages

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Gray-scale super-resolution for face recognition from low Gray-scale resolution face images

Gray-scale super-resolution for face recognition from low Gr...

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IEEE International Conference on Image processing

作者： Hu Han Shiguang Shan Xilin Chen Wen Gao Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Institute of Digital Media Peking University Beijing China

Today's camera sensors usually have a high gray-scale resolution, e.g. 256, however, due to the dramatic lighting variations, the gray-scales distributed to the face region might be far less than 256. Therefore, besides low spatial resolution, a practical face recognition system must also handle degraded face images of low gray-scale resolution (LGR). In the last decade, low spatial resolution problem has been studied prevalently, but LGR problem was rarely studied. Aiming at robust face recognition, this paper makes a first primary attempt to investigate explicitly the LGR problem and empirically reveals that LGR indeed degrades face recognition method significantly. Possible solutions to the problem are discussed and grouped into three categories: gray-scale resolution invariant features, gray-scale degradation modeling and Gray-scale Super-Resolution (GSR). Then, we propose a Coupled Subspace Analysis (CSA) based GSR method to recover the high gray-scale resolution image from a single input LGR image. Extensive experiments on FERET and CMU-PIE face databases show that the proposed method can not only dramatically increase the gray-scale resolution and visualization quality, but also impressively improve the accuracy of face recognition.

关键词： Image processing Conferences

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Fast rate-distortion optimized transform for Intra coding

Fast rate-distortion optimized transform for Intra coding

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Picture Coding Symposium, PCS

作者： Xin Zhao Li Zhang Siwei Ma Wen Gao Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Institute of Digital Media Peking University Beijing China

In our previous work, the rate-distortion optimized transform (RDOT) is introduced for Intra coding, which is featured by the usage of multiple offline-trained transform matrix candidates. The proposed RDOT achieves remarkable coding gain for KTA Intra coding, while maintaining almost the same computational complexity at the decoder. However, at the encoder, the computational complexity is increased drastically by the expensive ratedistortion (R-D) optimized selection of transform matrix. To resolve this problem, in this paper, we propose a fast RDOT scheme using macroblock- and block-level R-D cost thresholding. With the proposed method, unnecessary mode trials and R-D evaluations of transform matrices can be efficiently skipped from the mode decision process. Extensive experimental results show that, with negligible coding performance degradation, about 88.9% of the total encoding time is saved by the proposed method.

关键词： Encoding Transforms Rate-distortion Acceleration Image coding Algorithm design and analysis Computational complexity

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Entity answer extraction and homepage recognition

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Journal of information and Computational Science 2010年第1期7卷 277-283页

作者： Xian, Yantuan Yu, Zhengtao Xu, Yangbo Li, Lina Zou, Junjie Guo, Jianyi School of Information Engineering and Automation Kunming University of Science and Technology Kunming 650051 China Institute of Intelligent Information Processing Computer Technology Application Key Laboratory of Yunnan Province Kunming 650051 China

This paper proposed a method for entity answer extraction, which examined three levels of relevance, including document, passage and entity. The entity answer extraction system and homepage recognition are also described in detail. The results on TRECT 2009 test topics show the effectiveness of our approaches. Accuracy of entity answer extraction reaches 88.93% and accuracy of entity homepage recognition is 91.15%. Copyright © 2010 Binary information Press.

关键词： Extraction

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Maximizing intra-individual correlations for illumination-insensitive face recognition

Maximizing intra-individual correlations for illumination-in...

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IEEE International Conference on Image processing

作者： Hu Han Shiguang Shan Xilin Chen Wen Gao Chinese Academy of Sciences Beijing China Institute of Computing Technology Key Laboratory of Intelligent Information Processing Chinese Academy and Sciences Beijing China Institute of Digital Media Peking University Beijing China

Illumination variation has been one of the most intractable problems in face recognition and many approaches have been proposed to handle illumination problem in the last decades of years. The key problem is how to get stable similarity measurements between two face images of the same individual but captured under dramatically different lighting conditions. We propose a framework to optimize the illumination normalization for a pair of gallery and probe face images by maximizing a correlation (MAC) between them. The illumination normalization in the proposed framework tends to maximize the intra-individual correlations instead of both the inter- and intra-individual correlations. Experiments on Extended YaleB and CMU-PIE face databases show the effectiveness of our proposed approach in face recognition across varying lighting conditions.

关键词： Image processing Conferences

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A Bigtree Index for Resource Space Model

A Bigtree Index for Resource Space Model

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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： Zhongyi Zeng Hai Zhuge Department of Computer Science and Technology University of Science & Technology of China HefeiChi Knowledge Grid Research Group Key Lab of Intelligent Information Processing Institute of Computing

This paper presents the Bigtree, which is a hierarchical index for implementing the Resource Space Model.

关键词： Semantics Indexes Data models Humans USA Councils Springs Space technology

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Rate-distortion optimized transform for intra-frame coding

Rate-distortion optimized transform for intra-frame coding

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Xin Zhao Li Zhang Siwei Ma Wen Gao Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Institute of Digital Media Peking University Beijing China

In this paper, a novel algorithm is proposed for intra-frame coding, named as rate-distortion optimized transform (RDOT). Unlike existing intra-frame coding schemes where the transform matrices are either fixed or mode dependent, in the proposed algorithm, transform is implemented with multiple candidate transform matrices. With this flexibility, for coding each residual block, the encoder is endowed with the power to select the optimal transform matrix in terms of rate-distortion tradeoff. The proposed algorithm has been implemented in the latest ITU-T VCEG-KTA software. Experimental results show that, over a wide range of test set, the proposed method achieves average 0.43dB coding gain compared with the recent Mode-Dependent Directional Transform (MDDT). The improvement is more significant at high bit-rates, and up to 1dB coding gain can be achieved.

关键词： Transforms Encoding Image coding IP networks Training Decoding Gain

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Combining the Missing Link: An Incremental Topic Model of Document Content and Hyperlink

Combining the Missing Link: An Incremental Topic Model of Do...

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International Asia-Pacific Web Conference (APWEB)

作者： Huifang Ma Zhixin Li Zhongzhi Shi Chinese Academy and Sciences Beijing China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Chinese Academy of Sciences Beijing Beijing CN

The content and structure of linked information such as sets of web pages or research paper archives are dynamic and keep on changing. Even though different methods are proposed to exploit both the link structure and the content information, no existing approach can effectively deal with this evolution. We propose a novel joint model, called Link-IPLSI, to combine texts and links in a topic modeling framework incrementally. The model takes advantage of a novel link updating technique that can cope with dynamic changes of online document streams in a faster and scalable way. Furthermore, an adaptive asymmetric learning method is adopted to freely control the assignment of weights to terms and citations. Experimental results on two different sources of online information demonstrate the time saving strength of our method and indicate that our model leads to systematic improvements in the quality of classification and link prediction.

关键词： Laboratories intelligent structures information processing Computers Web pages Programmable control Adaptive control Learning systems Predictive models Data mining

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