Abstract
Image segmentation is a key step for image processing and Gaussian Mixture Models(GMMs) are the common models for segmentation. The EM algorithm is usually used to estimete the parameters of GMMs, which is opt to get stuck at local minimum. In this paper we propose a new initialized shceme, multiscale online learning, for EM to aviod local minima and for GMMs to decide the optimal initial number of components. Experimental results have shown that this scheme can effectively improve the precision of segmentation compared to classical EM algorithm.
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Guan, T., Xue, T. (2013). Image Segmentation Based on Multiscale Initialized Gaussian Mixtures. In: Yang, G. (eds) Proceedings of the 2012 International Conference on Communication, Electronics and Automation Engineering. Advances in Intelligent Systems and Computing, vol 181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31698-2_135
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DOI: https://doi.org/10.1007/978-3-642-31698-2_135
Publisher Name: Springer, Berlin, Heidelberg
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