A direct method to solve the biased discriminant analysis in kernel feature space for content based image retrieval

Dacheng Tao, Xiaoott Tang
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
more » ... discriminant analysis, which can overcome the matrix singular problem. The new method is shown to outperform the traditional kernel BDA and constrained support vector machine based relevance feedback algorithms.
doi:10.1109/icassp.2004.1326576 dblp:conf/icassp/TaoT04 fatcat:c2ev6jtkazhdjiwsbnlpgcox5m