Unsupervised image segmentation using a mean field decomposition of a posteriori probability

Hideki Noda; Mehdi N. Shirazi; Bing Zhang; Eiji Kawaguchi

doi:10.1117/12.334749

28 December 1998 Unsupervised image segmentation using a mean field decomposition of a posteriori probability

Hideki Noda, Mehdi N. Shirazi, Bing Zhang, Eiji Kawaguchi

Proceedings Volume 3653, Visual Communications and Image Processing '99; (1998) https://doi.org/10.1117/12.334749
Event: Electronic Imaging '99, 1999, San Jose, CA, United States

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.

Citation Download Citation

Hideki Noda, Mehdi N. Shirazi, Bing Zhang, and Eiji Kawaguchi "Unsupervised image segmentation using a mean field decomposition of a posteriori probability", Proc. SPIE 3653, Visual Communications and Image Processing '99, (28 December 1998); https://doi.org/10.1117/12.334749

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KEYWORDS

Image segmentation

Stochastic processes

Magnetorheological finishing

Image processing

Data modeling

Image analysis

Image processing algorithms and systems

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