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Image retrieval with relevance feedback: from heuristic weight adjustment to optimal learning methods

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Image retrieval with relevance feedback: from heuristic weight adjustment to optimal learning methods
Various relevance feedback algorithms have been proposed in recent years in the area of content-based image retrieval. This paper gives a brief review and analysis on existing techniques--from early heuristic-based feature weighting schemes to recently proposed optimal learning algorithms. In addition, the kernel-based biased discriminant analysis (KBDA) is proposed to fit the unique nature of relevance feedback as a biased classification problem. As a novel variant of traditional discriminant analysis, the proposed algorithm provides a trade-off between discriminant transform and regression. The kernel form is derived to deal with non-linearity in an elegant way. Experimental results indicate that significant improvement in retrieval performance is achieved by the new scheme.
Xiang Sean Zhou, Thomas S. Huang
Added 25 Oct 2009
Updated 25 Oct 2009
Type Conference
Year 2001
Where ICIP
Authors Xiang Sean Zhou, Thomas S. Huang
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