Direct kernel biased discriminant analysis: a new content-based image retrieval relevance feedback algorithm

Dacheng Tao, Xiaoou Tang, Xuelong Li, Yong Rui
2006 IEEE transactions on multimedia  
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
more » ... le 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. Index Terms-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).
doi:10.1109/tmm.2005.861375 fatcat:nhyax6v7irbyrijj7is5xejnhi