Abstract
Mixture Models(MMs) are a typical class of statistical models and have been applied to image processing in many situations, among which Gaussian MM (GMMs) are widely adopted. Main drawbacks of classical models involve that they need presetting the number of clusters, have not considered the influence of outliers. They will lead to unreasonable image segmentation results. This paper proposes the Self-Growing Regularized GMMs(SGRGMMs), which generalizes the classical GMMs, for image segmentation. We compute the unknown parameters using the self-branching competitive leaning and a new generalized EM algorithm, Regularized EM(REM). We carried out experiments on the segmentation of some images and our approach can automatically determine the number of clusters and efficiently erase the influence of outliers.
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Guan, T., Wang, H., Wang, Y. (2013). Self-Growing Regularized Gaussian Mixture Models for Image Segmentation. 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_82
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DOI: https://doi.org/10.1007/978-3-642-31698-2_82
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31697-5
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