Improved Similarity-Based Online Feature Selection in Region-Based Image Retrieval

To bridge the gap between high level semantic concepts and low level visual features in content-based image retrieval (CBIR), online feature selection is really required. An effective similarity-based online feature selection algorithm in region-based image retrieval (RBIR) systems was proposed by W. Jiang etc., but some parts of the algorithm need to be improved. In this paper, the above algorithm is modified in two aspects: (1) Adaptive mixture models based on mutual information theory are adopted to determine the codebook size. (2) A new method is proposed, which can select not only feature axes parallel to the original ones, but also combined feature axes. Experimental results on 10000 images show that the proposed method can improve the retrieval performance, and save the computational time