A Novel Multiple Instance Learning Approach for Image Retrieval Based on Adaboost Feature Selection

In image retrieval, the concepts are usually in region-level but annotated in image-level, which leads to a major difficulty in learning the target concepts. In this paper, we formulate region-based image retrieval as a multiple-instance learning (MIL) problem, and propose an efficient and effective algorithm, named MI-AdaBoost, to solve it. The algorithm firstly maps each bag into a new bag feature space using a certain set of instance prototypes, and then adopts AdaBoost to select the bag features and build classifiers simultaneously. Experiments on both COREL and MUSK datasets show the proposed scheme is much more efficient than some typical existing MIL algorithms while has comparable results.