02008nas a2200169   4500008004100000020001400041245011000055210006900165260001200234300001200246490000700258520136600265100001701631700001801648700002101666856015101687       1996                        eng d  a1077-316900aA Hierarchical Markov Random Field Model and Multitemperature Annealing for Parallel Image Classification0 aHierarchical Markov Random Field Model and Multitemperature Anne  c1996///  a18 - 370 v583 a<p>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). © 1996 Academic Press, Inc.</p>1 aKato, Zoltan1 aBerthod, Mark1 aZerubia, Josiane  uhttps://home.inf.u-szeged.hu/publication/a-hierarchical-markov-random-field-model-and-multitemperature-annealing-for-parallel-image-classification