This paper describes a Bayesian framework for performing relational graph matching by discrete relaxation. Our basic aim is to draw on this framework to provide a comparative evaluation of a number of contrasting approaches to relational matching. Broadly speaking there are two main aspects to this study. Firstly we locus on the issue of how relational inexactness may be quantified. We illustrate that several popular relational distance measures can be recovered as specific limiting cases of the Bayesian consistency measure. The second aspect of our comparison concerns the way in which structural inexactness is controlled. We investigate three different realizations ai the matching process which draw on contrasting control models. The main conclusion of our study is that the active process of graph-editing outperforms the alternatives in terms of its ability to effectively control a large population of contaminating clutter.
|Copyright, Publisher and Additional Information:||Copyright © 1997 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Keywords:||structural graph matching,discrete relaxation,energy minimization,Bayesian,graph edit,clutter,MAP estimation,SAR images,infrared images,PATTERN-RECOGNITION,RELATIONAL GRAPHS,COMPUTER VISION,IMAGES|
|Institution:||The University of York|
|Academic Units:||The University of York > Computer Science (York)|
|Depositing User:||Repository Officer|
|Date Deposited:||21 Feb 2007|
|Last Modified:||05 Mar 2017 01:21|