NDLI: Attributed tree homomorphism using association graphs

Content Provider	IEEE Xplore Digital Library
Author	Bartoli, M. Pelillo, M. Siddiqi, K. Zucker, S.W.
Copyright Year	2000
Description	Author affiliation: Dipt. di Inf., Univ. Ca' Foscari di Venezia, Venezia Mestre, Italy (Bartoli, M.)
Abstract	The matching of hierarchical relational structures is of significant interest in computer vision and pattern recognition. We have recently introduced a new solution to this problem, based on a maximum clique formulation in an (derived) "association graph". This allows us to exploit the full arsenal of clique finding algorithms developed in the algorithm community. However, thus far we have only focussed on one-to-one correspondences (isomorphisms), which appears to be too strict a requirement for many vision problems. In this paper we provide a generalization of the association graph framework to handle many-to-one correspondences. We define a notion of an /spl epsiv/-homomorphism (a many-to-one mapping) between attributed trees, and provide a method of constructing a weighted association graph where maximal weight cliques are in one-to-one correspondence with maximal similarity subtree homomorphisms. We then solve the problem by using replicator dynamical systems from the evolutionary game theory.
Starting Page	133
Ending Page	136
File Size	400975
Page Count	4
File Format	PDF
ISBN	0769507506
ISSN	10514651
DOI	10.1109/ICPR.2000.906033
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-09-03
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Tree graphs Computer vision Pattern matching Vegetation mapping Game theory Computational intelligence Machine intelligence Pattern recognition Object recognition Stereo vision
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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