Simple fast algorithms for the editing distance between trees and related problems
SIAM Journal on Computing
Structural Matching by Discrete Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The String-to-String Correction Problem
Journal of the ACM (JACM)
Computation of Normalized Edit Distance and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Least Committment Graph Matching by Evolutionary Optimisation
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Efficient Alignment and Correspondence Using Edit Distance
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Selection Strategies for Ambiguous Graph Matching by Evolutionary Optimisation
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Image categorization: Graph edit distance+edge direction histogram
Pattern Recognition
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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 used introduced for graph-matching by Sanfeliu and Fu. We show how the normalised edit distance of Marzal and Vidal 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.