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.
This research has been funded, in part, by the Army Research Laboratory’s Robotics Collaborative Technology Alliance program, contract number DAAD 19-012-0012 ARL-CTA-DJH.
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© 2006 Springer-Verlag Berlin Heidelberg
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Abd-Almageed, W., Davis, L.S. (2006). Density Estimation Using Mixtures of Mixtures of Gaussians. In: Leonardis, A., Bischof, H., Pinz, A. (eds) Computer Vision – ECCV 2006. ECCV 2006. Lecture Notes in Computer Science, vol 3954. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11744085_32
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DOI: https://doi.org/10.1007/11744085_32
Publisher Name: Springer, Berlin, Heidelberg
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