Bayesian graph edit distance

Citation
R. Myers et al., Bayesian graph edit distance, IEEE PATT A, 22(6), 2000, pp. 628-635
Citations number
29
Categorie Soggetti
AI Robotics and Automatic Control
Journal title
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
ISSN journal
01628828 → ACNP
Volume
22
Issue
6
Year of publication
2000
Pages
628 - 635
Database
ISI
SICI code
0162-8828(200006)22:6<628:BGED>2.0.ZU;2-N
Abstract
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 Leve nshtein distance can be used to model the probability distribution for stru ctural 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 rathe r than exponential in its worst-case complexity. We support our approach wi th an experimental study on synthetic data and illustrate its effectiveness on an uncalibrated stereo correspondence problem. This demonstrates experi mentally that the gain in efficiency is not at the expense of quality of ma tch.