Density Estimation Using Mixtures of Mixtures of Gaussians

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.

• Type: Conference
• Year: 2006
• Where: ECCV
• Authors: Wael Abd-Almageed, Larry S. Davis
• Keywords: Computer Vision, Density Estimation Algorithm, ECCV 2006, Expectation Maximization Algorithm, Model Selection Criterion, Penalty-less Information Criterion