Parallel image classification using multiscale Markov random fields

Zoltan Kato, Marc Berthod, Josiane Zerubia

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationImage and Multidimensional Signal Processing
PublisherPubl by IEEE
PagesV-137-V-140
ISBN (Print)0780309464
Publication statusPublished - Jan 1 1993
EventIEEE International Conference on Acoustics, Speech and Signal Processing, Part 5 (of 5) - Minneapolis, MN, USA
Duration: Apr 27 1993Apr 30 1993

Publication series

NameProceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
Volume5
ISSN (Print)0736-7791

Other

OtherIEEE International Conference on Acoustics, Speech and Signal Processing, Part 5 (of 5)
CityMinneapolis, MN, USA
Period4/27/934/30/93

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Kato, Z., Berthod, M., & Zerubia, J. (1993). Parallel image classification using multiscale Markov random fields. In Image and Multidimensional Signal Processing (pp. V-137-V-140). (Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing; Vol. 5). Publ by IEEE.