Finite general Gaussian mixture modeling and application to image and video foreground segmentation

Mohand Said Allili; Nizar Bouguila; Djemel Ziou

doi:10.1117/1.2898125

1 January 2008 Finite general Gaussian mixture modeling and application to image and video foreground segmentation

Mohand Said Allili, Nizar Bouguila, Djemel Ziou

Author Affiliations +

Journal of Electronic Imaging, Vol. 17, Issue 1, 013005 (January 2008). https://doi.org/10.1117/1.2898125

Abstract

We propose a new finite mixture model based on the formalism of general Gaussian distribution (GGD). Because it has the flexibility to adapt to the shape of the data better than the Gaussian, the GGD is less prone to overfitting the number of mixture classes when dealing with noisy data. In the first part of this work, we propose a derivation of the maximum likelihood estimation for the parameters of the new mixture model, and elaborate an information-theoretic approach for the selection of the number of classes. In the second part, we validate the proposed model by comparing it to the Gaussian mixture in applications related to image and video foreground segmentation.

©(2008) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Mohand Said Allili, Nizar Bouguila, and Djemel Ziou "Finite general Gaussian mixture modeling and application to image and video foreground segmentation," Journal of Electronic Imaging 17(1), 013005 (1 January 2008). https://doi.org/10.1117/1.2898125

Published: 1 January 2008

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