Unsupervised image segmentation using markov random fields

Authors:
Abdulkadir Şengür;İbrahim Türkoğlu;M. Cevdet İnce
Affiliations:
Department of Electronic and Computer Science, Fırat University, Elazı;Department of Electronic and Computer Science, Fırat University, Elazı;Department of Electric-Electronic Engineering, Fırat University, Elazı
Venue:
TAINN'05 Proceedings of the 14th Turkish conference on Artificial Intelligence and Neural Networks
Year:
2005

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Abstract

In this study, we carried out an unsupervised gray level image segmentation based on Markov Random Fields (MRF) model. First, we use the Expectation Maximization (EM) algorithm to estimate the distribution of the input image and the number of the components is automatically determined by the Minimum Message Length (MML) algorithm. Then the segmentation is done by the Iterated Conditional Modes (ICM) algorithm. For testing the segmentation performance, we use both artificial images and real images. The experimental results are satisfactory.