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 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.
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 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.}
}