@article{allili_finite_2008,

title = {Finite general Gaussian mixture modeling and application to image and video foreground segmentation},

author = {M. S. Allili and N. Bouguila and D. Ziou},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-78149489170&doi=10.1117%2f1.2898125&partnerID=40&md5=8a9b3060dda2366f17b22a06606a9f09},

doi = {10.1117/1.2898125},

issn = {10179909},

year  = {2008},

date = {2008-01-01},

journal = {Journal of Electronic Imaging},

volume = {17},

number = {1},

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 SPIE and IS&T.},

keywords = {Finite mixture models, Foreground segmentation, Gaussian distribution, Gaussian mixture modeling, Gaussian mixtures, Gaussians, General Gaussian distribution, Image segmentation, Information theory, Information-theoretic approach, Maximum likelihood estimation, Mixture model, Mixtures, Noisy data, Overfitting},

pubstate = {published},

tppubtype = {article}

}