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• W2002632913 abstract "Conventional approaches to content-based image retrieval exploit low-level visual information to represent images and relevance feedback techniques to incorporate human knowledge into the retrieval process, which can only alleviate the semantic gap to some extent. To further boost the performance, a Bayesian framework is proposed in which information independent of the visual content of images is utilized and integrated with the visual information. Two particular instances of the general framework are studied. First, context which is the statistical relation across the images is integrated with visual content such that the framework can extract information from both the images and past retrieval results. Second, characteristic sounds made by different objects are utilized along with their visual appearance. Based on various performance evaluation criteria, the proposed framework is evaluated using two databases for the two examples, respectively. The results demonstrate the advantage of the integration of information from multiple sources." @default.
• W2002632913 created "2016-06-24" @default.
• W2002632913 creator A5001287866 @default.
• W2002632913 creator A5065037360 @default.
• W2002632913 date "2010-01-01" @default.
• W2002632913 modified "2023-09-27" @default.
• W2002632913 title "A Bayesian Image Retrieval Framework" @default.
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• W2002632913 doi "https://doi.org/10.4018/jdls.2010040103" @default.
• W2002632913 hasPublicationYear "2010" @default.
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