Journal
INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
Volume 18, Issue 2-3, Pages 209-218Publisher
WILEY
DOI: 10.1002/ima.20146
Keywords
inexact graph matching; graph edit distance; hidden Markov model; Kullback-Leibler distance
Funding
- Program for New Century Excellent Talents in University [NCET-04-0948]
- University of China [IRT0645]
- National Natural Science Foundation of China [60771068, 60702061]
- Key Lab of ATR
- Shenzhen University, China
- Hong Kong Polytechnic University [A-PH42, A-PC0A]
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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.
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