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.

• Type: Conference
• Year: 2001
• Where: ICIP
• Authors: Xiang Sean Zhou, Thomas S. Huang
• Keywords: Biased Discriminant Analysis, ICIP 2001, Image Processing, Relevance Feedback Algorithms, Traditional Discriminant Analysis