TY - JOUR
T1 - Density estimation using mixtures of mixtures of Gaussians
JF - Computer Visionâ€“ECCV 2006
Y1 - 2006
A1 - Abd-Almageed, Wael
A1 - Davis, Larry S.
AB - 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.
ER -