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《Acta Electronica Sinica》 2011-10
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Combining Graph Learning and Region Saliency Analysis for Content-Based Image Retrieval

FENG Song-he1,2,LANG Cong-yan1,XU De1(1.School of Computer & Information Technology,Beijing Jiaotong University,Beijing 100044,China;2.Beijing Key Lab of Intelligent Telecommunications Software and Multimedia,Beijing University of Posts and Telecommunications,Beijing 100876,China)  
For the image retrieval task which combines machine learning theory with relevance feedback mechanism,this paper focuses on the graph-based semisupervised learning algorithm with application to region-based image retrieval.Different schemes which both incorporate the region saliency into the graph-based semi-supervised learning framework are applied to deal with two types of feedback.Firstly,in the case that no sample or only positive samples are available from the user's feedback,the retrieval task can be resolved via a transductive learning manner,a hierarchical graph model which incorporates region saliency information is constructed and the manifold-ranking algorithm is adopted subsequently for positive label propagation.Secondly,in the case that the user provides both positive and negative samples,the region-level adjacency matrix will be constructed via the feedback samples,and the manifold-ranking algorithm is also adopted here to choose instances which truly represent the user's query semantics.The selected instances are then used to retrieve the relevant samples.The experiments have proved the effectiveness of the proposed method.
【Fund】: 国家自然科学基金(No.60972145 61033013 No.61100142);; 中央高校基础科研业务费(No.2009JBM024);; 中国博士后科学基金(No.201003044);; 北京邮电大学智能通信软件与多媒体北京市重点实验室开放课题;; 北京市教育委员会科技发展计划(No.KM20091147002)
【CateGory Index】: TP391.41
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