Name
Abstract | ||
|---|---|---|
Many computer vision and pattern recognition problems involve the use of finite Gaussian mixture models. Finite mixture model using generalized Dirichlet distribution has been shown as a robust alternative of normal mixtures. In this paper, we adopt a Bayesian approach for generalized Dirichlet mixture estimation and selection. This approach, offers a solid theoretical framework for combining both the statistical model learning and the knowledge acquisition. The estimation of the parameters is based on the Monte Carlo simulation technique of Gibbs sampling mixed with a Metropolis-Hastings step. For the selection of the number of clusters, we used Bayes factors. We have successfully applied the proposed Bayesian framework to model IR eyes. Experimental results are shown to demonstrate the robustness, efficiency, and accuracy of the algorithm. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/CVPR.2007.383439 | CVPR |
Keywords | Field | DocType |
Bayes methods,Monte Carlo methods,computer vision,knowledge acquisition,sampling methods,Bayes factors,Bayesian nonGaussian mixture analysis,Gibbs sampling,Metropolis-Hastings step,Monte Carlo simulation,computer vision,eye modeling,finite Gaussian mixture models,finite mixture model,generalized Dirichlet distribution,knowledge acquisition,pattern recognition problems,statistical model learning | Pattern recognition,Computer science,Bayes factor,Generalized Dirichlet distribution,Gaussian,Artificial intelligence,Statistical model,Dirichlet distribution,Gibbs sampling,Mixture model,Bayesian probability | Conference |
Volume | Issue | ISSN |
2007 | 1 | 1063-6919 |
Citations | PageRank | References |
8 | 0.49 | 18 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nizar Bouguila | 1 | 1539 | 146.09 |
| Djemel Ziou | 2 | 1395 | 99.40 |
| Riad I. Hammoud | 3 | 118 | 9.46 |