Relevance Feedback Learning With Feature Selection In Region-Based Image Retrieval

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Region-based image retrieval and relevance feedback are two important methods to bridge the gap between the low-level visual features and the high-level semantic concepts in content-based image retrieval. In this paper, we address the issue of introducing the relevance feedback mechanism into the region-based image retrieval with online feature selection during each feedback round. Our contribution is two-fold. (1) A novel region-based image representation is proposed. Based on a generative model, a fuzzy codebook is extracted from the original region-based features, which represents the images in a uniform real-value feature space. (2) A feature selection criterion is developed and an effective relevance feedback algorithm is implemented in a boosting manner to simultaneously select the optimal features from the fuzzy codebook, and generate a strong ensemble classifier over the selected features. Experimental results show that the proposed scheme can substantially improve the retrieval performance of the region-based image retrieval system.