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, ICM, etc\ldots ). 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).
Reference:
Zoltan Kato, Josiane Zerubia, Mark Berthod, Unsupervised Parallel Image Classification Using a Hierarchical Markovian Model, In Proceedings of International Conference on Computer Vision, Cambridge, MA, USA, pp. 169-174, 1995.
Bibtex Entry:
@string{iccv="Proceedings of International Conference on Computer Vision"}
@InProceedings{Kato-etal95b,
author = {Kato, Zoltan and Zerubia, Josiane and Berthod, Mark},
title = {Unsupervised Parallel Image Classification Using a
Hierarchical {M}arkovian Model},
booktitle = iccv,
year = 1995,
pages = {169--174},
address = {Cambridge, MA, USA},
month = jun,
organization = {IEEE},
ps = {papers/iccv95.ps},
pdf = {papers/iccv95.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, ICM, etc\ldots
). 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).}
}