Parallel image classification using multiscale Markov random fields

  • Authors:

  • Zoltan Kato;Marc Berthod;Josiane Zerubia

  • Affiliations:

  • INRIA, Sophia Antipolis Cedex, France;INRIA, Sophia Antipolis Cedex, France;INRIA, Sophia Antipolis Cedex, France

  • Venue:
  • ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: image and multidimensional signal processing - Volume V
  • Year:
  • 1993

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Abstract

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.