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Proceedings Paper

Unsupervised image segmentation using a mean field decomposition of a posteriori probability
Author(s): Hideki Noda; Mehdi N. Shirazi; Bing Zhang; Eiji Kawaguchi
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Paper Abstract

This paper proposes a Markov random field (MRF) model-based method for unsupervised segmentation of 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. Simulation results show that the use of LAPs is essential to perform a good image segmentation.

Paper Details

Date Published: 28 December 1998
PDF: 8 pages
Proc. SPIE 3653, Visual Communications and Image Processing '99, (28 December 1998); doi: 10.1117/12.334749
Show Author Affiliations
Hideki Noda, Kyushu Institute of Technology (Japan)
Mehdi N. Shirazi, Osaka Institute of Technology (Japan)
Bing Zhang, Communications Research Lab. (Japan)
Eiji Kawaguchi, Kyushu Institute of Technology (Japan)


Published in SPIE Proceedings Vol. 3653:
Visual Communications and Image Processing '99
Kiyoharu Aizawa; Robert L. Stevenson; Ya-Qin Zhang, Editor(s)

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