Graph-based multiple-instance learning for object-based image retrieval

We study in this paper the problem of using multiple-instance semi-supervised learning to solve object-based image retrieval problem, in which the user is only interested in a portion of the image, and the rest of the image is considered as irrelevant. Although many multiple-instance learning (MIL) algorithms have been proposed to solve object-based image retrieval problem, most of them only have a supervised manner and do not fully utilize the information of the unlabeled data in the image collection. In this paper, to make use of the large amount of unlabeled data, we present a semi-supervised version of multiple-instance learning, i.e. multiple-instance semi-supervised learning (MISSL). By taking into account both the multiple-instance property and the semi-supervised property simultaneously, a novel regularization framework for MISSL is presented. Based on this framework, a graph-based multiple-instance learning (GMIL) algorithm is developed, in which three kinds of data, i.e. labeled data, semi-labeled data, and unlabeled data simultaneously propagate information on a graph. Moreover, under the same framework, GMIL can be reduced to a novel standard MIL algorithm (GMIL-M) by ignoring unlabeled data. We theoretically prove the convergence of the iterative solutions for GMIL and GMIL-M. We apply GMIL algorithm to solving object-based image retrieval problem, and experimental results show the superiority of the proposed method. Some experiments on standard MIL problems are also provided to show the competitiveness of the proposed algorithms compared with state-of-the-art MIL algorithms.