Title :
Unsupervised multistage segmentation using Markov random field and maximum entropy principle
Author :
Sanghoon, Lee ; Crawford, Melba M.
Author_Institution :
Dept. of Ind. Eng., Kyung Won Univ., Seongnam, South Korea
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
Keywords :
Bayes methods; Markov processes; data structures; image recognition; image segmentation; maximum entropy methods; random processes; smoothing methods; Markov random field; boundary blocking operation; computational efficiency; global smoothness; hierarchical clustering procedure; image processing; local spatial smoothness; maximum entropy principle; multiwindow approach; pyramid-like data structure; spatial contextual information; unsupervised multistage segmentation; Bayesian methods; Clustering algorithms; Digital images; Entropy; Geophysical measurements; Image segmentation; Industrial engineering; Layout; Markov random fields; Pixel;
Conference_Titel :
Image Processing, 1994. Proceedings. ICIP-94., IEEE International Conference
Conference_Location :
Austin, TX
Print_ISBN :
0-8186-6952-7
DOI :
10.1109/ICIP.1994.413558
