Abstract
This thesis presents a region-based image retrieval approach with relevance feedback by taking the region correspondence into consideration. Region representation in image retrieval has been popular issues in the recent works because global features are insufficient to describe the local variations within images. Region correspondence estimation is one of the critical problems in region-based image similarity comparison. Intuitively, we can estimate the region correspondence by minimizing the matching error of matched regions. Since human perception matches images depending not only on their local attributes but also on their interrelationships, we must take the relationships of region connectivity into consideration when estimating the region correspondence. To estimate the region correspondence by considering the region attributes and the relationships of region connectivity, we represent images by graphs in which the nodes represent the regions and the edges represent the relationships of region connectivity. We then solve the region correspondence estimation problem via graph matching technique. However, the graph matching technique, which matches non-attributed graphs, does not completely solve our region correspondence problem. Thus, we modify the graph matching algorithm to satisfy our requirements. To further improve the retrieval results, we formulate the relevance feedback process as a maximum likelihood framework. We show that the maximization of the likelihood function has a closed form solution. The ideal query image, the region weights, and the feature weights are updated by the feedback images and their region correspondences. In experimental results, a series of experiments show that the proposed approach achieves good performance for various natural images with complex contents.