Image retrieval based on multi-concept detector and semantic correlation

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• 作者：HaiJiao Xu ; ChangQin Huang ; Peng Pan ; GanSen Zhao…
• 关键词：multi ; concept image retrieval ; semantic correlation ; probability estimation ; concept learning ; visual evidence ; 澶氭蹇靛浘鍍忔绱?/li> 璇箟鍏宠仈 ; 姒傜巼浼扮畻 ; 姒傚康瀛︿範 ; 瑙嗚璇佹嵁 ; 122104
• 刊名：SCIENCE CHINA Information Sciences
• 出版年：2015
• 出版时间：December 2015
• 年：2015
• 卷：58
• 期：12
• 页码：1-15
• 全文大小：1,301 KB
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• 作者单位：HaiJiao Xu (1) (2)
  ChangQin Huang (2) (4)
  Peng Pan (1)
  GanSen Zhao (2)
  ChunYan Xu (3)
  YanSheng Lu (1)
  Deng Chen (1)
  JiYi Wu (4)
  
  1. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
  2. Research Center for Information Services and Software Technology, South China Normal University, Guangzhou, 510631, China
  4. E-Service Research Center, Zhejiang University, Hangzhou, 310027, China
  3. Electrical and Computer Engineering, National University of Singapore, 21 Lower Kent Ridge Road, 119077, Singapore, Singapore
  
• 刊物类别：Computer Science
• 刊物主题：Chinese Library of Science
  Information Systems and Communication Service
  
• 出版者：Science China Press, co-published with Springer
• ISSN：1869-1919

文摘

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. Keywords multi-concept image retrieval semantic correlation probability estimation concept learning visual evidence