Region correspondence for image retrieval using graph-theoretic approach and maximum likelihood estimation

This paper proposes employing an efficient graph-theoretic approach to estimate the region correspondence between two images. We represent each image as an attributed graph and transform the image matching problem into a graph matching problem. During the image retrieval process, we formulate the matching problem as a maximum likelihood estimation and propose an optimization technique to derive its closed-form solution. Hence, we are capable of measuring the image distance in terms of both the estimated region correspondence and the low-level features. This paper has two main contributions. First, our proposed matching technique is efficient and applicable to the interactive process of image retrieval. Second, with the estimated region correspondence, the proposed matching criterion, which is defined in terms of matched regions and penalized with unmatched regions, achieves satisfactory performance for retrieval applications.