Tao, D. and Tang, X. and Li, Xuelong and Rui, Y. (2006) Direct kernel biased discriminant analysis: a new content-based image retrieval relevance feedback algorithm. IEEE Transactions on Multimedia 8 (4), pp. 716-727. ISSN 1520-9210.
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In recent years, a variety of relevance feedback (RF) schemes have been developed to improve the performance of content-based image retrieval (CBIR). Given user feedback information, the key to a RF scheme is how to select a subset of image features to construct a suitable dissimilarity measure. Among various RF schemes, biased discriminant analysis (BDA) based RF is one of the most promising. It is based on the observation that all positive samples are alike, while in general each negative sample is negative in its own way. However, to use BDA, the small sample size (SSS) problem is a big challenge, as users tend to give a small number of feedback samples. To explore solutions to this issue, this paper proposes a direct kernel BDA (DKBDA), which is less sensitive to SSS. An incremental DKBDA (IDKBDA) is also developed to speed up the analysis. Experimental results are reported on a real-world image collection to demonstrate that the proposed methods outperform the traditional kernel BDA (KBDA) and the support vector machine (SVM) based RF algorithms.
|Additional Information:||This is an exact copy of a paper published in IEEE Transactions on Multimedia (ISSN 1520-9210). This material is posted here with permission of the IEEE. Copyright © 2006 IEEE.|
|Keyword(s) / Subject(s):||biased discriminant analysis (BDA), contentbased image retrieval (CBIR), direct kernel biased discriminant analysis (DKBDA), incremental direct kernel biased discriminant analysis (IDKBDA), kernel biased discriminant analysis (KBDA), relevance feedback (RF)|
|School or Research Centre:||Birkbeck Schools and Research Centres > School of Business, Economics & Informatics > Computer Science and Informatics|
|Depositing User:||Sandra Plummer|
|Date Deposited:||30 Jan 2007|
|Last Modified:||17 Apr 2013 12:33|
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