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PAMI
2008

Simplifying Mixture Models Using the Unscented Transform

8 years 5 months ago
Simplifying Mixture Models Using the Unscented Transform
Mixture of Gaussians (MoG) model is a useful tool in statistical learning. In many learning processes that are based on mixture models, computational requirements are very demanding due to the large number of components involved in the model. We propose a novel algorithm for learning a simplified representation of a Gaussian mixture that is based on the Unscented Transform, which was introduced for filtering nonlinear dynamical systems. The superiority of the proposed method is validated on both simulation experiments and categorization of a real image database. The proposed categorization methodology is based on modeling each image using a Gaussian mixture model. A category model is obtained by learning a simplified mixture model from all the images in the category.
Jacob Goldberger, Hayit Greenspan, Jeremie Dreyfus
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2008
Where PAMI
Authors Jacob Goldberger, Hayit Greenspan, Jeremie Dreyfuss
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