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Bayesian graph edit distance

Myers, R., Hancock, E.R. (orcid.org/0000-0003-4496-2028) and Wilson, R.C. (2000) Bayesian graph edit distance. IEEE Transactions on Pattern Analysis and Machine Intelligence. pp. 628-635. ISSN 0162-8828

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This paper describes a novel framework for comparing and matching corrupted relational graphs. The paper develops the idea of edit-distance originally introduced for graph-matching by Sanfeliu and Fu [1]. We show how the Levenshtein distance can be used to model the probability distribution for structural errors in the graph-matching problem. This probability distribution is used to locate matches using MAP label updates. We compare the resulting graph-matching algorithm with that recently reported by Wilson and Hancock. The use of edit-distance offers an elegant alternative to the exhaustive compilation of label dictionaries. Moreover, the method is polynomial rather than exponential in its worst-case complexity. We support our approach with an experimental study on synthetic data and illustrate its effectiveness on an uncalibrated stereo correspondence problem. This demonstrates experimentally that the gain in efficiency is not at the expense of quality of match.

Item Type: Article
Copyright, Publisher and Additional Information: Copyright © 2000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Keywords: Graph matching,edit-distance,Bayesian,MAP estimation,stereo images
Institution: The University of York
Academic Units: The University of York > Computer Science (York)
Depositing User: Repository Officer
Date Deposited: 21 Feb 2007
Last Modified: 09 May 2016 11:03
Published Version: http://dx.doi.org/10.1109/34.862201
Status: Published
Refereed: Yes
URI: http://eprints.whiterose.ac.uk/id/eprint/1988

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