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Unsupervised multistage segmentation using Markov random field and maximum entropy principle

Sanghoon Lee(1) and Melba M. Crawford (2)

(1): Dept. of Industrial Engineering, Kyung Won University, Seongnam, South Korea
(2): Center for Space Research, University of Texas at Austin

E-mail: crawford@csr.utexas.edu

ABSTRACT

A multistage algorithm which makes use of spatial contextual information in a hierarchical clustering procedure has been developed for unsupervised image segmentation. A Markov random field model is employed to enforce local spatial smoothness, while the maximum entropy principle is utilized to quantify global smoothness in the image processing. A multiwindow approach implemented in a pyramid-like data structure which uses a boundary blocking operation is employed to increase computational efficiency.

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Last Modified: Tue July 13, 1999
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