Technical Program

Paper Detail

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; Universit√© de Sherbrooke 
 Djemel Ziou; Universit√© 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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