HMM-based graph edit distance for image indexing

Most of the existing graph edit distance (GED) algorithms require cost functions which are difficult to be defined exactly. In this article, we propose a cost function free algorithm for computing GED. It only depends on the distribution of nodes rather than node or edge attributes in graphs. Hidden Markov model (HMM) is employed to model the distribution of feature points and thus dissimilarity measure of graphs can be posed as distance of HMMs. A fast algorithm of Kullback-Leibler Distance, suitable for computing the distance between two probability models, is adopted to compute the distance of HMMs. Experimental results demonstrate that the proposed GED algorithm can characterize the structure variety of graphs effectively and is available for clustering and indexing images of both rigid and nonrigid bodies. (c) 2008 Wiley Periodicals, Inc.