Title
A Hierarchical Markov Random Field Model and Multitemperature Annealing for Parallel Image Classification
Abstract
In this paper, we are interested in massively parallel multiscale relaxation algorithms applied to image classification. It is well known that multigrid methods can improve significantly the convergence rate and the quality of the final results of iterative relaxation techniques. First, we present a classical multiscale model which consists of a label pyramid and a whole observation field. The potential functions of coarser grids are derived by simple computations. The optimization problem is first solved at the higher scale by a parallel relaxation algorithm; then the next lower scale is initialized by a projection of the result. Second, we propose a hierarchical Markov random field model based on this classical model. We introduce new interactions between neighbor levels in the pyramid. It can also be seen as a way to incorporate cliques with far apart sites for a reasonable price. This model results in a relaxation algorithm with a new annealing scheme: the multitemperature annealing (MTA) scheme, which consists of associating higher temperatures to higher levels, in order to be less sensitive to local minima at coarser grids. The convergence to the global optimum is proved by a generalization of the annealing theorem of S. Geman and D. Geman (IEEE Trans. Pattern Anal. Mach. Intell.6, 1984, 721–741).
Year
DOI
Venue
1996
10.1006/gmip.1996.0002
Graphical Models and Image Processing
Keywords
Field
DocType
image classification,statistical physics,optimization problem,classical groups,image processing,local minima,multigrid method,multiscale modeling,renormalization group,convergence rate
Random field,Iterative method,Markov random field,Markov model,Parallel algorithm,Algorithm,Maxima and minima,Rate of convergence,Multigrid method,Mathematics
Journal
Volume
Issue
ISSN
58
1
1077-3169
Citations 
PageRank 
References 
43
4.86
8
Authors
3
Name
Order
Citations
PageRank
Zoltan Kato126528.28
Marc Berthod2429163.29
Josiane Zerubia32032232.91