Unsupervised adaptive image segmentation

Author

Kato, Zoltan ; Zerubia, Josiane ; Berthod, Marc ; Pieczynski, W.

Author_Institution

INRIA, Sophia Antipolis, France

Volume

4

fYear

1995

fDate

9-12 May 1995

Firstpage

2399

Abstract

This paper deals with the problem of unsupervised Bayesian segmentation 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 available image only. Our approach consists of a recent iterative method of estimation, called iterative conditional estimation (ICE), applied to a monogrid Markovian image segmentation model. The method has been tested on synthetic and real satellite images

Keywords

Bayes methods; Markov processes; adaptive estimation; image segmentation; iterative methods; parameter estimation; unsupervised learning; Markov random fields; adaptive image segmentation; hidden label field parameters; iterative conditional estimation; monogrid Markovian image segmentation model; parameter estimation; real satellite images; synthetic images; unsupervised Bayesian segmentation; Bayesian methods; Equations; Ice; Image segmentation; Iterative algorithms; Iterative methods; Labeling; Markov random fields; Parameter estimation; Satellites; Simulated annealing; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1995. ICASSP-95., 1995 International Conference on

Conference_Location

Detroit, MI

ISSN

1520-6149

Print_ISBN

0-7803-2431-5

Type

conf

DOI

10.1109/ICASSP.1995.479976

Filename

479976