Unsupervised parallel image classification using Markovian models

Zoltan Kato, Josiane Zerubia, Marc Berthod

Research output: Contribution to journalArticle

69 Citations (Scopus)


This paper deals with the problem of unsupervised classification of images modeled by Markov random fields (MRF). If the model parameters are known then we have various methods to solve the segmentation problem (simulated annealing (SA), iterated conditional modes (ICM), etc). However, when the parameters are unknown, the problem becomes more difficult. One has to estimate the hidden label field parameters only from the observed image. Herein, we are interested in parameter estimation methods related to monogrid and hierarchical MRF models. The basic idea is similar to the expectation-maximization (EM) algorithm: we recursively look at the maximum a posteriori (MAP) estimate of the label field given the estimated parameters, then we look at the maximum likelihood (ML) estimate of the parameters given a tentative labeling obtained at the previous step. The only parameter supposed to be known is the number of classes, all the other parameters are estimated. The proposed algorithms have been implemented on a Connection Machine CM200. Comparative experiments have been performed on both noisy synthetic data and real images.

Original languageEnglish
Pages (from-to)591-604
Number of pages14
JournalPattern Recognition
Issue number4
Publication statusPublished - Apr 1999


  • Hierarchical model
  • Markov random field model
  • Parallel unsupervised image classification
  • Parameter estimation

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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