@article {13185,
	title = {Density estimation using mixtures of mixtures of Gaussians},
	journal = {Computer Vision{\textendash}ECCV 2006},
	year = {2006},
	month = {2006///},
	pages = {410 - 422},
	abstract = {In this paper we present a new density estimation algorithm using mixtures of mixtures of Gaussians. The new algorithm overcomes the limitations of the popular Expectation Maximization algorithm. The paper first introduces a new model selection criterion called the Penalty-less Information Criterion, which is based on the Jensen-Shannon divergence. Mean-shift is used to automatically initialize the means and covariances of the Expectation Maximization in order to obtain better structure inference. Finally, a locally linear search is performed using the Penalty-less Information Criterion in order to infer the underlying density of the data. The validity of the algorithm is verified using real color images.},
	author = {Abd-Almageed, Wael and Davis, Larry S.}
}
