Graph-based multiple-instance learning with instance weighting for image retrieval

Object-based image retrieval has been an active research topic in recent years, in which user only pays his attention to some object in the images. As one promising approach, multiple-instance learning has attracted many researchers. Most of recently proposed methods either need additional restrictions for instance selection or lead to heavy computational load, so that they are often inconvenient for practical applications. In this paper, a novel method based on weighting regions in positive images is proposed, which mainly includes two steps of graph-based learning. The first step is only conducted on regions in training images, and different weights are efficiently set to each region in positive images based on the learning results. The second step is conducted on regions of all the database images, regions in positive images are fully utilized without selection, and ranking scores for each image are calculated. Experimental results demonstrate the effectiveness of our proposal.