An efficient unsupervised mixture model for image segmentation

Authors:
Pan Lin;XiaoJian Zheng;Gang Yu;ZuMao Weng;Sheng Zhen Cai
Affiliations:
Faculty of Software of Fujian Normal University, Fuzhou, P.R. China;Faculty of Software of Fujian Normal University, Fuzhou, P.R. China;School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, P.R. China;Faculty of Software of Fujian Normal University, Fuzhou, P.R. China;Faculty of Software of Fujian Normal University, Fuzhou, P.R. China
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Year:
2006

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Abstract

In this paper, we present an efficient unsupervised mixture model image segmentation method. The idea of this method is that individual image region classes are modeled as mixtures of fuzzy subclasses of mixture distributions, and classification is performed based on the Expectation-Maximization algorithm. To overcome the difficulty of classical mixture model method for noisy image segmentation, spatial contextual information should be taken into account. In particular, the proposed approach based on Markov Random Field was shown to provide more accurate classification of images than traditional Expectation-Maximization algorithm and traditional Markov Random Field image segmentation techniques. The effectiveness of the proposed method is illustrated with synthetic and real images data. The experiments results have shown that the proposed method can achieve more robust segmentation for noisy images.