Multi-graph multi-instance learning for object-based image and video retrieval
Fei Li, Rujie Liu · 2012
Object-based image retrieval has been an active research topic in recent years, in which a user is only interested in some object in the images. As one promising approach, graph-based multi-instance learning has attracted many researchers. The existing methods often conduct learning on one graph, either in image level or in region level. While in this paper, by considering both image- and region-level information at the same time, a novel method based on multi-graph multi-instance learning is proposed. Two graphs are constructed in our method, and the relationship between each image and its segmented regions is introduced into an optimization framework. Moreover, our method is further extended to video retrieval. By exploring the relationships between video shots, representative images, and segmented regions, it can deal with the case when training labels are only assigned in shot level. Experimental results on the SIVAL image benchmark and the TRECVID video set demonstrate the effectiveness of our proposal.