Attributed Tree Homomorphism Using Association Graphs

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 a (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 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 -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 evolutionary game theo...

• Type: Conference
• Year: 2000
• Where: ICPR
• Authors: Massimo Bartoli, Marcello Pelillo, Kaleem Siddiqi, Steven W. Zucker
• Keywords: Association Graph Framework, Computer Vision, ICPR 2000, Maximal Weight Cliques, Weighted Association Graph