An algorithm of object-based image retrieval using multiple instance learning
Chao Wen, Guohua Geng, Xinyi Zhu · 2011
For the problem of object-based image retrieval, in this paper a novel semi-supervised multiple instance learning algorithm is presented. In the framework of multiple instance learning, this algorithm regards the whole image as a bag, and low-level visual feature of the segmented regions as instances. Firstly, the algorithm clusters the instances in two sets, one of which is composed of instances in positive bags and the other is composed of instances in negative bags, so as to find potential positive instances and feature data of bag structure. Then their respective similarities are measured by radial basis function, and an alpha coefficient is introduced in bag similarity measure as the trade-off between the two similarities. Experiments on SIVAL dataset show that this algorithm is feasible and the performance is superior to other algorithms.