Wael AbdAlmageed

Density Estimation Using Mixtures of Mixtures of Gaussians

TitleDensity Estimation Using Mixtures of Mixtures of Gaussians
Publication TypeConference Paper
Year of Publication2006
AuthorsW. Abd-almageed, and L. S. Davis
Conference NameEuropean Conference on Computer Vision (ECCV)
Date Publishedmay

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.