Unsupervised learning of a finite mixture model based on the Dirichlet distribution and its application.
This paper presents an unsupervised algorithm for learning a finite mixture model from multivariate data. This mixture model is based on the Dirichlet distribution, which offers high flexibility for modeling data. The proposed approach for estimating the parameters of a Dirichlet mixture is based on the maximum likelihood (ML) and Fisher scoring methods. Experimental results are presented for the following applications: estimation of artificial histograms, summarization of image databases for efficient retrieval, and human skin color modeling and its application to skin detection in multimedia databases.
Bouguila
Nizar
N
Département d'Informatique, Université de Sherbrooke, QC, Canada. nizar.bouguila@usherbrooke.ca
Ziou
Djemel
D
Vaillancourt
Jean
J
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003124:Colorimetry / Q000379:methods*; D003196:Computer Graphics; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods; D008954:Models, Biological; D015233:Models, Statistical; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D012880:Skin Pigmentation / Q000502:physiology*; D016008:Statistical Distributions; D013382:Subtraction Technique; D014584:User-Computer Interface