Parallel image classification using multiscale Markov random fields

The application of massively parallel multiscale relaxation algorithms to image classification is considered. First, a classical multiscale model applied to supervised image classification is presented. The model consists of a label pyramid and a whole observation field. The potential functions of the coarse grid are derived by simple computations. Then, a scheme which introduces a local interaction between two neighbor grids in the label pyramid is proposed. This is a way to incorporate cliques, with far-apart sites for a reasonable price. Finally, results on noisy synthetic data and on a SPOT image obtained by different relaxation methods using these models are presented.<>