Density Estimation Using Mixtures of Mixtures of Gaussians
Title | Density Estimation Using Mixtures of Mixtures of Gaussians |
Publication Type | Conference Paper |
Year of Publication | 2006 |
Authors | W. Abd-almageed, and L. S. Davis |
Conference Name | European Conference on Computer Vision (ECCV) |
Date Published | may |
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. |
URL | http://link.springer.com/chapter/10.1007/11744085_32 |
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