Graph matching with hierarchical discrete relaxation

Our aim in this paper is to develop a Bayesian framework for matching hiera rchical relational models. Such models are widespread in computer vision. T he 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 optimi se a global cost function that draws information concerning the consistency of match from different levels of the hierarchy. Our Bayesian development naturally distinguishes between intra-level and inter-level constraints. Th is 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 childr en in the hierarchy. We illustrate the effectiveness of the technique in th e matching of line-segment groupings in synthetic aperture radar (SAR) imag es of rural scenes. (C) 1999 Elsevier Science B.V. All rights reserved.