Multi-graph multi-instance learning with soft label consistency for object-based image retrieval

Object-based image retrieval has been an active research topic in the last decade, in which a user is only interested in some object instead of the whole image. As a promising approach, graph-based multi-instance learning has been paid much attention. Early retrieval methods often conduct learning on one graph in either image or region level. To further improve the performance, some recent methods adopt multi-graph learning, but the relationship between image- and region-level information is not well explored. In this paper, by constructing both image- and region-level graphs, a novel multi-graph multi-instance learning method is proposed. Different from the existing methods, the relationship between each labeled image and its segmented regions is reflected by the consistency of their corresponding soft labels, and it is formulated by the mutual restrictions in an optimization framework. A comprehensive cost function is designed to involve all the available information, and an iterative solution is introduced to solve the problem. Experimental results on the benchmark data set demonstrate the effectiveness of our proposal.