TY - CHAP T1 - Parallel image classification using multiscale Markov random fields T2 - ICASSP-93 Y1 - 1993 A1 - Zoltan Kato A1 - Marc Berthod A1 - Josiane Zerubia ED - *IEEE Signal Pro *Society ED - *Institute of Electri *Engineers AB - In this paper, we are interested in massively parallel multiscale relaxation algorithms applied to image classification. First, we present a classical multiscale model applied to supervised image classification. 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, we propose another scheme introducing a local interaction between two neighbor grids in the label pyramid. This is a way to incorporate cliques with far apart sites for a reasonable price. Finally we present the results on noisy synthetic data and on a SPOT image obtained by different relaxation methods using these models. JF - ICASSP-93 PB - IEEE CY - New York N1 - ScopusID: 0027266514doi: 10.1109/ICASSP.1993.319766 ER -