Infinite generalized gaussian mixture modeling and applications

Authors:
Tarek Elguebaly;Nizar Bouguila
Affiliations:
Concordia Institute for Information Systems Engineering, Concordia University, Montreal, Canada, Qc;Concordia Institute for Information Systems Engineering, Concordia University, Montreal, Canada, Qc
Venue:
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
Year:
2011

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Abstract

A fully Bayesian approach to analyze infinite multidimensional generalized Gaussian mixture models (IGGM) is developed in this paper. The Bayesian framework is used to avoid model overfitting and the infinite assumption is adopted to avoid the difficult problem of finding the right number of mixture components. The utility of the proposed approach is demonstrated by applying it on texture classification and infrared face recognition, while comparing it to different other approaches.