Spatial Finite Non-gaussian Mixture for Color Image Segmentation

A new color image segmentation algorithm based on the integration of spatial information into finite generalized Dirichlet mixture models is presented. The integration of spatial information is done via the consideration of image pixels neighborhoods. The segmentation model presented is learned using maximum likelihood estimation within an expectation maximization (EM) optimization framework. The obtained results, evaluated quantitatively, using real images are very encouraging and are better than those obtained using similar approaches.

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