A multigrid approach to the Gibbsian classification of mammograms

Both formal and informal locally-adaptive cooling schedules have been suggested to improve the convergence rate of Gibbs (and Gibbs-like) classification algorithms. One strategy involves maintaining a global cooling schedule/visiting schedule which is turned on or off (or forcing extremal temperature values) at a site depending on the interiteration behaviour of the classifier. This (0,1)-valued behaviour of the cooling schedule is parameterized relative to the site. We give a preliminary discussion of a method of assigning such parameters based on a multigrid decomposition of the image. The application domain is mammography.