Density estimation using mixtures of mixtures of Gaussians

TitleDensity estimation using mixtures of mixtures of Gaussians
Publication TypeJournal Articles
Year of Publication2006
AuthorsAbd-Almageed W, Davis LS
JournalComputer Vision–ECCV 2006
Pagination410 - 422
Date Published2006///

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.