DocumentCode :
1123924
Title :
Maximum-likelihood parameter estimation for unsupervised stochastic model-based image segmentation
Author :
Zhang, Jun ; Modestino, James W. ; Langan, David A.
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Wisconsin Univ., Milwaukee, WI, USA
Volume :
3
Issue :
4
fYear :
1994
fDate :
7/1/1994 12:00:00 AM
Firstpage :
404
Lastpage :
420
Abstract :
An unsupervised stochastic model-based approach to image segmentation is described, and some of its properties investigated. In this approach, the problem of model parameter estimation is formulated as a problem of parameter estimation from incomplete data, and the expectation-maximization (EM) algorithm is used to determine a maximum-likelihood (ML) estimate. Previously, the use of the EM algorithm in this application has encountered difficulties since an analytical expression for the conditional expectations required in the EM procedure is generally unavailable, except for the simplest models. In this paper, two solutions are proposed to solve this problem: a Monte Carlo scheme and a scheme related to Besag´s (1986) iterated conditional mode (ICM) method. Both schemes make use of Markov random-field modeling assumptions. Examples are provided to illustrate the implementation of the EM algorithm for several general classes of image models. Experimental results on both synthetic and real images are provided
Keywords :
Markov processes; Monte Carlo methods; image segmentation; iterative methods; maximum likelihood estimation; minimisation; parameter estimation; EM algorithm; Markov random-field modeling; Monte Carlo method; conditional expectations; expectation-maximization algorithm; image models; image segmentation; iterated conditional mode method; maximum-likelihood parameter estimation; synthetic images; unsupervised stochastic model; Algorithm design and analysis; Image segmentation; Information processing; Markov random fields; Maximum likelihood estimation; Parameter estimation; Research and development; Stochastic processes; Systems engineering and theory; Training data;
fLanguage :
English
Journal_Title :
Image Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1057-7149
Type :
jour
DOI :
10.1109/83.298395
Filename :
298395
Link To Document :
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