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Infinite Generalized Gaussian Mixture Modeling and Applications

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Image Analysis and Recognition (ICIAR 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6753))

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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.

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Author information

Authors and Affiliations

  1. Concordia Institute for Information Systems Engineering, Concordia University, Montreal, Canada, Qc, H3G 2W1

    Tarek Elguebaly & Nizar Bouguila

Authors
  1. Tarek Elguebaly
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  2. Nizar Bouguila
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Waterloo, N2L 3G1, Waterloo, ON, Canada

    Mohamed Kamel

  2. Faculty of Engineering, Institute of Biomedical Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Aurélio Campilho

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Elguebaly, T., Bouguila, N. (2011). Infinite Generalized Gaussian Mixture Modeling and Applications. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2011. Lecture Notes in Computer Science, vol 6753. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21593-3_21

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  • DOI: https://doi.org/10.1007/978-3-642-21593-3_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21592-6

  • Online ISBN: 978-3-642-21593-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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