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Journal of Electronic Imaging

Finite general Gaussian mixture modeling and application to image and video foreground segmentation
Author(s): Mohand Said Allili; Nizar Bouguila; Djemel Ziou
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Paper 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.

Paper Details

Date Published: 1 January 2008
PDF: 13 pages
J. Electron. Imag. 17(1) 013005 doi: 10.1117/1.2898125
Published in: Journal of Electronic Imaging Volume 17, Issue 1
Show Author Affiliations
Mohand Said Allili, Univ. de Sherbrooke (Canada)
Nizar Bouguila, Concordia Univ. (Canada)
Djemel Ziou, Univ. de Sherbrooke (Canada)

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