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Unsupervised texture based image segmentation by simulated annealing using Markov random field and Potts models

Goktepe, M
Atalay, Mehmet Volkan
Yalabik, N
Yalabik, C
Unsupervised segmentation of images which are composed of various textures is investigated A coarse segmentation is achieved through a hierarchical self organizing map. This initial segmentation result is fed into a simulated annealing algorithm in which region and texture parameters are estimated using maximum likelihood technique. Region geometries are modeled as Potts model while textures are modeled as Markov random fields. Tests are performed an artificial textured images.