Mean Field Decomposition of a Posteriori Probability for MRF-Based Image Segmentation: Unsupervised Multispectral Textured Image Segmentation

Hideki NODA; Mehdi N. SHIRAZI; Bing ZHANG; Nobuteru TAKAO; Eiji KAWAGUCHI

Mean Field Decomposition of a Posteriori Probability for MRF-Based Image Segmentation: Unsupervised Multispectral Textured Image Segmentation

Hideki NODA, Mehdi N. SHIRAZI, Bing ZHANG, Nobuteru TAKAO, Eiji KAWAGUCHI

Full Text Views

0

Share
Cite this

Summary :

This paper proposes a Markov random field (MRF) model-based method for unsupervised segmentation of multispectral images consisting of multiple textures. To model such textured images, a hierarchical MRF is used with two layers, the first layer representing an unobservable region image and the second layer representing multiple textures which cover each region. This method uses the Expectation and Maximization (EM) method for model parameter estimation, where in order to overcome the well-noticed computational problem in the expectation step, we approximate the Baum function using mean-field-based decomposition of a posteriori probability. Given provisionally estimated parameters at each iteration in the EM method, a provisional segmentation is carried out using local a posteriori probability (LAP) of each pixel's region label, which is derived by mean-field-based decomposition of a posteriori probability of the whole region image. Experiments show that the use of LAPs is essential to perform a good image segmentation.

Publication: IEICE TRANSACTIONS on Information Vol.E82-D No.12 pp.1605-1611

Publication Date: 1999/12/25

Publicized

Online ISSN

DOI

Type of Manuscript: PAPER

Category: Image Processing,Computer Graphics and Pattern Recognition

Cite this

Copy

Hideki NODA, Mehdi N. SHIRAZI, Bing ZHANG, Nobuteru TAKAO, Eiji KAWAGUCHI, "Mean Field Decomposition of a Posteriori Probability for MRF-Based Image Segmentation: Unsupervised Multispectral Textured Image Segmentation" in IEICE TRANSACTIONS on Information, vol. E82-D, no. 12, pp. 1605-1611, December 1999, doi: .
Abstract: This paper proposes a Markov random field (MRF) model-based method for unsupervised segmentation of multispectral images consisting of multiple textures. To model such textured images, a hierarchical MRF is used with two layers, the first layer representing an unobservable region image and the second layer representing multiple textures which cover each region. This method uses the Expectation and Maximization (EM) method for model parameter estimation, where in order to overcome the well-noticed computational problem in the expectation step, we approximate the Baum function using mean-field-based decomposition of a posteriori probability. Given provisionally estimated parameters at each iteration in the EM method, a provisional segmentation is carried out using local a posteriori probability (LAP) of each pixel's region label, which is derived by mean-field-based decomposition of a posteriori probability of the whole region image. Experiments show that the use of LAPs is essential to perform a good image segmentation.
URL: https://globals.ieice.org/en_transactions/information/10.1587/e82-d_12_1605/_p

Copy

@ARTICLE{e82-d_12_1605,
author={Hideki NODA, Mehdi N. SHIRAZI, Bing ZHANG, Nobuteru TAKAO, Eiji KAWAGUCHI, },
journal={IEICE TRANSACTIONS on Information},
title={Mean Field Decomposition of a Posteriori Probability for MRF-Based Image Segmentation: Unsupervised Multispectral Textured Image Segmentation},
year={1999},
volume={E82-D},
number={12},
pages={1605-1611},
abstract={This paper proposes a Markov random field (MRF) model-based method for unsupervised segmentation of multispectral images consisting of multiple textures. To model such textured images, a hierarchical MRF is used with two layers, the first layer representing an unobservable region image and the second layer representing multiple textures which cover each region. This method uses the Expectation and Maximization (EM) method for model parameter estimation, where in order to overcome the well-noticed computational problem in the expectation step, we approximate the Baum function using mean-field-based decomposition of a posteriori probability. Given provisionally estimated parameters at each iteration in the EM method, a provisional segmentation is carried out using local a posteriori probability (LAP) of each pixel's region label, which is derived by mean-field-based decomposition of a posteriori probability of the whole region image. Experiments show that the use of LAPs is essential to perform a good image segmentation.},
keywords={},
doi={},
ISSN={},
month={December},}

Copy

TY - JOUR
TI - Mean Field Decomposition of a Posteriori Probability for MRF-Based Image Segmentation: Unsupervised Multispectral Textured Image Segmentation
T2 - IEICE TRANSACTIONS on Information
SP - 1605
EP - 1611
AU - Hideki NODA
AU - Mehdi N. SHIRAZI
AU - Bing ZHANG
AU - Nobuteru TAKAO
AU - Eiji KAWAGUCHI
PY - 1999
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E82-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 1999
AB - This paper proposes a Markov random field (MRF) model-based method for unsupervised segmentation of multispectral images consisting of multiple textures. To model such textured images, a hierarchical MRF is used with two layers, the first layer representing an unobservable region image and the second layer representing multiple textures which cover each region. This method uses the Expectation and Maximization (EM) method for model parameter estimation, where in order to overcome the well-noticed computational problem in the expectation step, we approximate the Baum function using mean-field-based decomposition of a posteriori probability. Given provisionally estimated parameters at each iteration in the EM method, a provisional segmentation is carried out using local a posteriori probability (LAP) of each pixel's region label, which is derived by mean-field-based decomposition of a posteriori probability of the whole region image. Experiments show that the use of LAPs is essential to perform a good image segmentation.
ER -