Kernel full-space biased discriminant analysis

Recently, relevance feedback has been widely used to improve the performance of content-based image retrieval. How to select a subset of features from a large-scale feature pool and to construct a suitable dissimilarity measure are key steps in a relevance feedback system. Biased discriminant analysis (BDA) has been proposed to select features during relevance feedback iterations. However, to solve the BDA, we often encounter the matrix singular problem. Motivated by the direct method and null-space method successfully used in the Fisher linear discriminant analysis for face recognition, we generalize them into the Hilbert space for BDA. Because the direct method and the null-space method may lose some discriminant information, we propose a new full-space method to contain all discriminant information. We also generalize the full-space method into the Hilbert space. All the new methods are demonstrated to outperform the traditional kernel BDA based relevance feedback algorithms based on a statistical experiment in the Corel database with 17, 800 images