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International Journal of Digital Content technology and its Applications 2011年第7期5卷 335-349页

作者： Yang, Kun Huang, Rui Lin, Fen Shi, Zhongzhi Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences 100190 Beijing China National Institute of Metrology 100013 Beijing China

This paper explores heterogeneous semantic data in Web 1.0, Semantic Web and Web 2.0 for topicspecific crawling and search. A statistical Semantic Association Model (SAM) is proposed to support semantic interoperability among four different models of thesauruses, categories, ontologies, and folksonomies. Based on this model, a focused crawling and semantic search framework is developed. In focused crawling of potentially related textual and semantic data, URLs are ordered before crawling and irrelevant Web pages are filtered out after crawling according to SAM-based semantic relevance ranking. In order that the retrieved results are more semantically related to the user queries, approaches of SAM-based semantic query expansion and meta-search result aggregation are designed. Experiments show that the proposed model and framework effectively integrates both keyword data and heterogeneous semantic data for topic-specific crawling and search.

关键词： Semantic Web

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Moving object classification method based on SOM and K-means

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Journal of Computers 2011年第8期6卷 1654-1661页

作者： Wu, Jian Xia, Jie Chen, Jian-ming Cui, Zhi-ming The Institute of Intelligent Information Processing and Application Soochow University Suzhou 215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou 215006 China

We do research on moving object classification in traffic video. Our aim is to classify the moving objects into pedestrians, bicycles and vehicles. Due to the advantage of self-organizing feature map (SOM), an unsupervised learning algorithm, which is simple and self organization, and the common usage of K-means clustering method, this paper combines SOM with K-means to do classification of moving objects in traffic video, constructs a system including four parts, and proposes a method based on bidirectional comparison of centroid to do tracking, and an improved method to obtain initial background when using background subtraction method to detect motion of moving objects. Experimental results show the effectiveness and robustness of the proposed approach. © 2011 ACADEMY PUBLISHER.

关键词： Self organizing maps

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The Estimation of Winter Wheat Yield Based on MODIS Remote Sensing Data

The Estimation of Winter Wheat Yield Based on MODIS Remote S...

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Computer and computing Technologies in Agriculture V——5th IFIP TC 5/SIG 5.1 Conference(CCTA2011) Part II

作者： Linsheng Huang Qinying Yang Dong Liang Yansheng Dong Xingang Xu Wenjiang Huang Key Laboratory of Intelligent Computing & Signal Processing Ministry of EducationAnhui University Beijing Research Center for Information Technology in Agriculture

ISBN: (纸本)9783642272776

A yield estimation method by remote sensing was used to estimate the yield of winter wheat in Jiangsu province,*** first step of this study was to extract the planting area of winter wheat from environmental satellite images and land-use map of Jiangsu province,meanwhile,correlation analyses were performed by using 8-day of composite Leaf Area Index(LAI)data from Moderate Resolution Imaging Spectroradiometer(MODIS)and statistical yield of corresponding ***,the average LAI was calculated at the optimal growth period,and the statistical yields of wheat for all counties were collected,in which the former was chosen as the independent variable and the latter was the dependent variable,and the regression model was ***,the accuracy and stability of the regression model were validated using the data of another *** results indicated that the yield estimation model at provincial level was reliable,the Root Mean Square Error(RMSE)and the Mean Absolute Error(MAE)of the model was 12.1%and 9.7%,*** addition,the yield estimation system of winter wheat in Jiangsu province was constructed and published based on ArcMap and ArcGIS Server.

关键词： LAI remote sensing yield estimation regression model ArcGIS Server

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GPU based fast algorithm for tanner graph based image interpolation

GPU based fast algorithm for tanner graph based image interp...

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IEEE Workshop on Multimedia Signal processing

作者： Wei Lei Ruiqin Xiong Siwei Ma Luhong Liang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences (CAS) Beijing China Institute of Digital Media Peking University Beijing China

In image/video processing software and hardware products, low complexity interpolation algorithms, such as cubic and splines methods, are commonly used. However, these methods tend to blur textures and produce jaggy effect compared with other adaptive methods such as NEDI, SAI. Tanner graph based image interpolation algorithm has better effect in dealing with edge and texture, but with high computation complexity. Thanks to the high performance parallel processing capability of today's GPU, use of complex algorithms for real time application is becoming possible. In this paper, we present a fast algorithm for tanner graph based image interpolation and it's implementation on GPU. In our algorithm, the image model training process of tanner graph based image interpolation is greatly simplified. Experimental results show that the GPU implementation can be more than 47 times as fast as the CPU implementation.

关键词： Graphics processing unit Interpolation Training Instruction sets Parallel processing Equations Image resolution

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DH-TRIE frequent pattern mining on Hadoop using JPA

DH-TRIE frequent pattern mining on Hadoop using JPA

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2011 IEEE International Conference on Granular computing, GrC 2011

作者： Yang, Lai Shi, Zhongzhi Xu, Li D. Liang, Fan Kirsh, Ilan 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 Dept. of ITDS Old Dominion University Norfolk VA 23529 United States Academic College of Tel-Aviv Yaffo Israel

ISBN: (纸本)9781457703713

The FPgrowth is a famous frequent pattern's algorithm in data mining when working with high-dimensional, large-scale data sets. It is also known as great complexity on memory for the recursively processing. In general, FPgrowth cannot handle large-scale data set unless dividing a whole data set into small blocks. Based on Hadoop, the open cloud computing model, a distributed DH-TRIE frequent pattern algorithm using JPA is proposed, which solved the three problems (globalization, random-write and duration). The algorithm is shown good flexibility and scalability by comparisons to mahout project. By applied to a virtualization platform Vega Cloud, the algorithm will be used in far-ranging situations. © 2011 IEEE.

关键词： Cloud computing

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Fast common visual pattern detection via radiate geometric model

Fast common visual pattern detection via radiate geometric m...

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IEEE International Conference on Image processing

作者： Lingyang Chu Shuqiang Jiang Qingming Huang Key Laboratory of Intelligent Information Processing Chinese Academy of Sciences China Institute of Computing Technology CAS Beijing China Chinese Academy of Sciences Beijing China

In this paper, we propose a novel method to implement fast detection of Common Visual Pattern (CVP). The purpose of CVP detection is to find the correspondences between the common visual regions of two given partial duplicate images. There are two major components of the proposed method which guarantee the good performance. First, we establish the Radiate-Geometric-Model (RGM). The RGM is represented by a set of radiate structures, and each structure is geometrically made up of a group of matched feature pairs. By utilizing the statistical information gained from the radiate structures, the RGM can not only quickly estimate the potential pairs of common regions but also organize the scale relationship between matched pairs into a compact form, hence increase the detection speed substantially. Second, we formulize the Radiate-Geometric-Model (RGM) into a graph optimization problem which could be solved by the method of graph-shift, thus make our algorithm capable of detecting the CVPs of all kinds of correspondences. Experimental results prove that the speed of our algorithm is at least 40 times faster than the state-of-the-art, while achieving a better detection performance at the same time.

关键词： Accuracy Histograms Visualization Optimization Feature extraction Conferences Vectors

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A fuzzy support vector machine algorithm with dual membership based on hypersphere

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Journal of Computational information Systems 2011年第6期7卷 2028-2034页

作者： Ding, Shifei Gu, Yaxiang School of Computer Science and Technology China University of Mining and Technology Xuzhou 221116 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Science Beijing 100080 China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing 100876 China

In traditional fuzzy support vector machine(FSVM), membership function is established in global scope will reduce the membership of support vectors, and the FSVM based dismissing margin increases the training speed, but will remove some support vector artificially. So, a new algorithm of Fuzzy Support Vector Machine with Dual Membership based on Hypersphere (HDM-FSVM) is proposed. In this algorithm, the two classes of hyperspheres are divided into two parts respectively. Then, according to most support vectors are in the hemispheres which close together, we use the membership function that can enhance the membership of support vector, and because of there are a few of support vectors in other hemispheres, we must ensure the high membership of support vectors and reduce the membership of non-support vector. In order to removal noise and outliers, we introduce a radius controlling factor to control size of hyperspheres, the samples that outside of hyperspheres are considered as noise and outliers. Experimental results show that HDM-FSVM can enhance the classification accuracy rate of the sample sets that contain noise and outliers. Copyright © 2011 Binary information Press.

关键词： Membership functions

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A Survey on the Resource Space Model

A Survey on the Resource Space Model

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The Seventh International Conference on Semantics,Knowledge,and Grids(第七届语义、知识与网格国际会议 SKG 20110)

作者： Jingqiang Chen Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 P.R.China

This paper surveys research on the Resource Space Model RSM. RSM is a classification-based, multi-dimensional and content-based space model for efficiently and effectively managing various resources. As a non-relational data model, it has a rather complete theoretical basis and has significant applications in faceted search and the future cyber-physical society. Applications in picture resources and email resources are introduced.

关键词： Electronic mail Semantics Computational modeling Peer to peer computing Yttrium XML Data models

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MST based stream data clustering algorithm

International Journal of Digital Content Technology and its ...

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International Journal of Digital Content technology and its Applications 2011年第9期5卷 352-360页

作者： Cheng, Zheng Xiaodong, Zhu Jingcheng, Li Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education Anhui University China School of Computer Science and Technology Anhui University China Institute of Information Management and Electronic Business University of Shanghai for Science and Technology China

How to make use of limited memory space and processing speeds of computer for rapid and accurate data mining has become an important research topic on the stream data cluster analysis. A stream data clustering algorithm based on the minimum spanning tree (MSTSC) is described. MSTSC is divided into online processing and offline clustering. Stream data are analyzed online by using two groups of processing unit respectively. In offline process clusters is taken as representative objects, and the minimum spanning tree algorithm is used in offline clustering. MSTSC can improve the clustering quality on non-spherical clusters. Some experiments are carried out in both real data sets and synthetic data sets. Results show that MSTSC algorithm not only can deal with non-spherical clusters effectively, but also has better efficiency and clustering quality. In addition, MSTSC is insensitive to order of input data, and has a good effect for skewed class distributions.

关键词： Cluster analysis

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Multi-kernel Co-SVM with a new strategy using unlabeled data for image retrieval

Multi-kernel Co-SVM with a new strategy using unlabeled data...

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International Conference on Pervasive computing and Applications (ICPCA)

作者： Li Zongmin Liu Yang Li Hua School of Computer and Communications Engineering China University of Petroleum Shandong China Key Laboratory Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781457702099

Relevance feedback based on SVM classifier shows a good performance recently but the finite feedback counts limited by user's patience and the small sample size problem are not solved well, Co-SVM does a good job in solving these problems but still has some flaws. We propose three strategies to try to improve this algorithm: (1) different kernel functions are used to characterize the color and texture visual similarities; (2) a new method is proposed to caculate the confident scores of the contention samples; (3) a bunch of the most irrelevant images with the highest confident score are added into the labeled images to extend the size of labeled data while choosing a bunch of images for user labeling. Experimental results verify the superiority of our method over Co-SVM.

关键词： Support vector machines Multimedia communication Classification algorithms

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