Unsupervised parallel image classification using a hierarchical Markovian model

Z. Kato, Josiane Zerubia, Marc Berthod

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

8 Citations (Scopus)

Abstract

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, ICM, etc...). However, when they are not known, the problem becomes more difficult. One has to estimate the hidden label field parameters from the only observable image. Our approach consists of extending a recent iterative method of estimation, called Iterative Conditional Estimation (ICE) to a hierarchical markovian model. The idea resembles the Estimation-Maximization (EM) algorithm as 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. We propose unsupervised image classification algorithms using a hierarchical model. The only parameter supposed to be known is the number of regions, all the other parameters are estimated. The presented algorithms have been implemented on a Connection Machine CM200. Comparative tests have been done on noisy synthetic and real images (remote sensing).

Original languageEnglish
Title of host publicationIEEE International Conference on Computer Vision
Editors Anon
PublisherIEEE
Pages169-174
Number of pages6
Publication statusPublished - 1995
EventProceedings of the 5th International Conference on Computer Vision - Cambridge, MA, USA
Duration: Jun 20 1995Jun 23 1995

Other

OtherProceedings of the 5th International Conference on Computer Vision
CityCambridge, MA, USA
Period6/20/956/23/95

Fingerprint

Image classification
Labels
Iterative methods
Simulated annealing
Labeling
Maximum likelihood
Remote sensing

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Kato, Z., Zerubia, J., & Berthod, M. (1995). Unsupervised parallel image classification using a hierarchical Markovian model. In Anon (Ed.), IEEE International Conference on Computer Vision (pp. 169-174). IEEE.

Unsupervised parallel image classification using a hierarchical Markovian model. / Kato, Z.; Zerubia, Josiane; Berthod, Marc.

IEEE International Conference on Computer Vision. ed. / Anon. IEEE, 1995. p. 169-174.

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

Kato, Z, Zerubia, J & Berthod, M 1995, Unsupervised parallel image classification using a hierarchical Markovian model. in Anon (ed.), IEEE International Conference on Computer Vision. IEEE, pp. 169-174, Proceedings of the 5th International Conference on Computer Vision, Cambridge, MA, USA, 6/20/95.
Kato Z, Zerubia J, Berthod M. Unsupervised parallel image classification using a hierarchical Markovian model. In Anon, editor, IEEE International Conference on Computer Vision. IEEE. 1995. p. 169-174
Kato, Z. ; Zerubia, Josiane ; Berthod, Marc. / Unsupervised parallel image classification using a hierarchical Markovian model. IEEE International Conference on Computer Vision. editor / Anon. IEEE, 1995. pp. 169-174
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