Graph Matching with Hierarchical Discrete Relaxation

Part of Advances in Neural Information Processing Systems 10 (NIPS 1997)

Bibtex Metadata Paper


Richard Wilson, Edwin Hancock


Our aim in this paper is to develop a Bayesian framework for match(cid:173) ing hierarchical relational models. The goal is to make discrete la(cid:173) bel assignments so as to optimise a global cost function that draws information concerning the consistency of match from different lev(cid:173) els of the hierarchy. Our Bayesian development naturally distin(cid:173) guishes between intra-level and inter-level constraints. This allows the impact of reassigning a match to be assessed not only at its own (or peer) level ofrepresentation, but also upon its parents and children in the hierarchy.