Simplifying mixture models through function approximation
IEEE Transactions on Neural Networks
Texture classification by modeling joint distributions of local patterns with Gaussian mixtures
IEEE Transactions on Image Processing
KDE Paring and a Faster Mean Shift Algorithm
SIAM Journal on Imaging Sciences
Levels of details for gaussian mixture models
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Incremental object learning and robust tracking of multiple objects from RGB-D point set data
Journal of Visual Communication and Image Representation
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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.