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A simulated annealing version of the EM algorithm for non-Gaussian deconvolution
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Improve maximum likelihood estimation for subband GGD parameters
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A Robust Video Foreground Segmentation by Using Generalized Gaussian Mixture Modeling
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A fast estimation method for the generalized Gaussian mixture distribution on complex images
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An equivalence of the EM and ICE algorithm for exponential family
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Analysis of multiresolution image denoising schemes using generalized Gaussian and complexity priors
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IEEE Transactions on Information Theory
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IEEE Transactions on Information Theory
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Estimation of generalized mixtures and its application in image segmentation
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Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance
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Infinite generalized gaussian mixture modeling and applications
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The infinite Student's t-mixture for robust modeling
Signal Processing
Spatial color image segmentation based on finite non-Gaussian mixture models
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This paper presents a fully Bayesian approach to analyze finite generalized Gaussian mixture models which incorporate several standard mixtures, widely used in signal and image processing applications, such as Laplace and Gaussian. Our work is motivated by the fact that the generalized Gaussian distribution (GGD) can be applied on a wide range of data due to its shape flexibility which justifies its usefulness to model the statistical behavior of multimedia signals [1]. We present a method to evaluate the posterior distribution and Bayes estimators using a Gibbs sampling algorithm. For the selection of number of components in the mixture, we use the integrated likelihood and Bayesian information criteria. We validate the proposed method by applying it to: synthetic data, real datasets, texture classification and retrieval, and image segmentation; while comparing it to different other approaches.