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A direct method to solve the biased discriminant analysis in kernel feature space for content based image retrieval
2004 IEEE International Conference on Acoustics, Speech, and Signal Processing
In recent years, relevance feedback has been widely used to improve the performance of content-based image retrieval. How to select a subset of features from a largescale feature pool and to construct a suitable dissimilarity measure are key steps in a relevance feedback system. Biased discriminant analysis has been proposed to select features during relevance feedback iterations. However, to solve the BDA, we often encounter the matrix singular problem. In this paper, we propose a kernel-based
doi:10.1109/icassp.2004.1326576
dblp:conf/icassp/TaoT04
fatcat:c2ev6jtkazhdjiwsbnlpgcox5m