### 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 language | English |
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Title of host publication | IEEE International Conference on Computer Vision |

Editors | Anon |

Publisher | IEEE |

Pages | 169-174 |

Number of pages | 6 |

Publication status | Published - 1995 |

Event | Proceedings of the 5th International Conference on Computer Vision - Cambridge, MA, USA Duration: Jun 20 1995 → Jun 23 1995 |

### Other

Other | Proceedings of the 5th International Conference on Computer Vision |
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City | Cambridge, MA, USA |

Period | 6/20/95 → 6/23/95 |

### Fingerprint

### ASJC Scopus subject areas

- Engineering(all)

### Cite this

*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.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*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.

}

TY - GEN

T1 - Unsupervised parallel image classification using a hierarchical Markovian model

AU - Kato, Z.

AU - Zerubia, Josiane

AU - Berthod, Marc

PY - 1995

Y1 - 1995

N2 - 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).

AB - 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).

UR - http://www.scopus.com/inward/record.url?scp=0029214757&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0029214757&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0029214757

SP - 169

EP - 174

BT - IEEE International Conference on Computer Vision

A2 - Anon, null

PB - IEEE

ER -