| Paper: | MLSP-L3.6 | ||
| Session: | Learning Theory and Modeling | ||
| Time: | Friday, May 21, 17:10 - 17:30 | ||
| Presentation: | Lecture | ||
| Topic: | Machine Learning for Signal Processing: Learning Theory and Modeling | ||
| Title: | DIRICHLET-BASED PROBABILITY MODEL APPLIED TO HUMAN SKIN DETECTION | ||
| Authors: | Nizar Bouguila; Université de Sherbrooke | ||
| Djemel Ziou; Université de Sherbrooke | |||
| Abstract: | The performance of a statistical signal processing system depends in large part on the accuracy of the probabilistic model used. This paper presents a robust probabilistic mixture model based on a generalization of the Dirichlet distribution. An unsupervised algorithm for learning this mixture is given, too. The proposed approachfor estimating the parameters of a Dirichlet mixture is based on the maximum Likelihood (ML) and fisher SCoring Methods. Experimenatl results involve human skin color modeling and its application to skin detection in images. | ||
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