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VISION:集成分类法、主题词表和语义元数据的概念网络 被引量:23
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作者 王军 《情报学报》 CSSCI 北大核心 2003年第4期412-418,共7页
本文提出了一种在分类法和主题词表的基础上集成语义元数据、构建概念网络、实现概念检索的方法.和其他的概念检索系统相比,它的最大特色是在检索之前先将信息资源根据其内容和主题组织到概念网络中.这样的概念网络,既是一个资源组织的... 本文提出了一种在分类法和主题词表的基础上集成语义元数据、构建概念网络、实现概念检索的方法.和其他的概念检索系统相比,它的最大特色是在检索之前先将信息资源根据其内容和主题组织到概念网络中.这样的概念网络,既是一个资源组织的框架,又是一个知识浏览和概念检索的信息空间.同时,还能支持用户学习.文章介绍了国内外概念检索的研究现状,讨论了集成分类法、主题词表和语义元数据构建概念网络的方法和好处.介绍了一个原型系统VISION,它是在<中国分类主题词表>的基础上,利用北京大学图书馆计算机类的书目数据实现的.文章最后进行深入讨论并介绍下一步的研究工作. 展开更多
关键词 集成分类法 主题词表 概念检索系统 信息组织 语义元数据 VISION 概念网络 关键词检索
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Image retrieval based on multi-concept detector and semantic correlation 被引量:2
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作者 XU HaiJiao HUANG ChangQin +5 位作者 PAN Peng ZHAO GanSen XU ChunYan LU YanSheng CHEN Deng WU JiYi 《Science China Chemistry》 SCIE EI CAS CSCD 2015年第12期100-114,共15页
With the rapid development of future network, there has been an explosive growth in multimedia data such as web images. Hence, an efficient image retrieval engine is necessary. Previous studies concentrate on the sing... With the rapid development of future network, there has been an explosive growth in multimedia data such as web images. Hence, an efficient image retrieval engine is necessary. Previous studies concentrate on the single concept image retrieval, which has limited practical usability. In practice, users always employ an Internet image retrieval system with multi-concept queries, but, the related existing approaches are often ineffective because the only combination of single-concept query techniques is adopted. At present semantic concept based multi-concept image retrieval is becoming an urgent issue to be solved. In this paper, a novel Multi-Concept image Retrieval Model(MCRM) based on the multi-concept detector is proposed, which takes a multi-concept as a whole and directly learns each multi-concept from the rearranged multi-concept training set. After the corresponding retrieval algorithm is presented, and the log-likelihood function of predictions is maximized by the gradient descent approach. Besides, semantic correlations among single-concepts and multiconcepts are employed to improve the retrieval performance, in which the semantic correlation probability is estimated with three correlation measures, and the visual evidence is expressed by Bayes theorem, estimated by Support Vector Machine(SVM). Experimental results on Corel and IAPR data sets show that the approach outperforms the state-of-the-arts. Furthermore, the model is beneficial for multi-concept retrieval and difficult retrieval with few relevant images. 展开更多
关键词 multi-concept image retrieval semantic correlation probability estimation concept learning visualevidence
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