| Paper: | IMDSP-P7.6 | ||
| Session: | Indexing and Retrieval | ||
| Time: | Wednesday, May 19, 15:30 - 17:30 | ||
| Presentation: | Poster | ||
| Topic: | Image and Multidimensional Signal Processing: Image and Video Indexing and Retrieval | ||
| Title: | A DIRECT METHOD TO SOLVE THE BIASED DISCRIMINANT ANALYSIS IN KERNEL FEATURE SPACE FOR CONTENT BASED IMAGE RETRIEVAL | ||
| Authors: | Dacheng Tao; Chinese University of Hong Kong | ||
| Xiaoou Tang; Chinese University of Hong Kong | |||
| Abstract: | In recent years, relevance feedback has been wildly 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. In this paper, we propose a kernel-based 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. | ||
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