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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Keywords
- Mixture Model
- Face Recognition
- Gaussian Mixture Modeling
- Cosine Distance
- Generalize Gaussian Distribution
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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
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