Unsupervised Parallel Image Classification Using Markovian Models

Kato, Zoltan; Zerubia, Josiane; Berthod, Mark

by Zoltan Kato, Josiane Zerubia, Mark Berthod

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 (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 expectationmaximization (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.

Reference:

Zoltan Kato, Josiane Zerubia, Mark Berthod, Unsupervised Parallel Image Classification Using Markovian Models, In Pattern Recognition, volume 32, no. 4, pp. 591-604, 1999.

Bibtex Entry:

@string{pattrec="Pattern Recognition"}
@Article{Kato-etal99,
  author =	 {Kato, Zoltan and Zerubia, Josiane and Berthod, Mark},
  title =	 {Unsupervised Parallel Image Classification Using
                  {M}arkovian Models},
  journal =	 pattrec,
  year =	 1999,
  volume =	 32,
  number =	 4,
  pages =	 {591--604},
  month =	 apr,
  keywords =	 {Markov random field model; Hierarchical model;
                  Parameter estimation; Parallel unsupervised image
                  classification},
  pdf =		 {papers/pattrec99.pdf},
  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 (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 expectationmaximization (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.}
}