Abstract
The segmentation of color image is an important research field of image processing and pattern recognition. A color image could be considered as the result from Gaussian mixture model (GMM) to which several Gaussian random variables contribute. In this paper, an efficient method of image segmentation is proposed. The method uses Gaussian mixture models to model the original image, and transforms segmentation problem into the maximum likelihood parameter estimation by expectation-maximization (EM) algorithm. And using the method to classify their pixels of the image, the problem of color image segmentation can be resolved to some extent. The experiment results confirm this method validity.
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Fu, Z., Wang, L. (2012). Color Image Segmentation Using Gaussian Mixture Model and EM Algorithm. In: Wang, F.L., Lei, J., Lau, R.W.H., Zhang, J. (eds) Multimedia and Signal Processing. CMSP 2012. Communications in Computer and Information Science, vol 346. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35286-7_9
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DOI: https://doi.org/10.1007/978-3-642-35286-7_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35285-0
Online ISBN: 978-3-642-35286-7
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