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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Lin, P., Zheng, X., Yu, G., Weng, Z., Cai, S.Z. (2006). An Efficient Unsupervised Mixture Model for Image Segmentation. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_42
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DOI: https://doi.org/10.1007/11893257_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
Online ISBN: 978-3-540-46482-2
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