Graph Matching with Hierarchical Discrete Relaxation

Our aim in this paper is to develop a Bayesian framework for matching hierarchical relational models. Such models are widespread in computer vision. The framework that we adopt for this study is provided by iterative discrete relaxation. Here the aim is to assign the discrete matches so as to optimise a global cost function that draws information concerning the consistency of match from di€erent levels of the hierarchy. Our Bayesian development naturally distinguishes 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 of representation, but also upon its parents and children in the hierarchy. We illustrate the e€ectiveness of the technique in the matching of line-segment groupings in synthetic aperture radar (SAR) images of rural scenes. Ó 1999 Elsevier Science B.V. All rights reserved.

• Type: Conference
• Year: 1997
• Where: NIPS
• Authors: Richard C. Wilson, Edwin R. Hancock
• Keywords: Bayesian Framework, Hierarchical Relational Models, Iterative Discrete Relaxation, NIPS 1997, NIPS 2007