An MRF model-based method for unsupervised textured image segmentation

This paper proposes a Markov random field (MRF) model-based method for unsupervised segmentation of images consisting of multiple textures. This method uses a hierarchical MRF with two layers, the first layer representing an unobservable region image and the second layer representing multiple textur...

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Bibliographic Details
Published inProceedings of 13th International Conference on Pattern Recognition Vol. 2; pp. 765 - 769 vol.2
Main Authors Noda, H., Shirazi, M.N., Kawaguchi, E.
Format Conference Proceeding
LanguageEnglish
Japanese
Published IEEE 1996
Subjects
Annealing
Geometry
Image segmentation
Iterative methods
Markov random fields
Maximum likelihood estimation
Parameter estimation
Pixel
Relaxation methods
Stochastic processes
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ISBN9780818672828
081867282X
ISSN1051-4651
DOI10.1109/ICPR.1996.546926

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Summary:This paper proposes a Markov random field (MRF) model-based method for unsupervised segmentation of images consisting of multiple textures. This method uses a hierarchical MRF with two layers, the first layer representing an unobservable region image and the second layer representing multiple textures which cover each region. This method is an iterative method based on the framework of the expectation and maximization (EM) method. We make use of an approximation for the Baum function in the expectation step. This reduces the parameter estimation to the maximum likelihood (ML) estimation given the current estimate of the region image. An estimation of the region image (image segmentation) is carried out by a deterministic relaxation method proposed by us.
ISBN:9780818672828
081867282X
ISSN:1051-4651
DOI:10.1109/ICPR.1996.546926