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Title:      BAYESIAN BASED CLASSIFIER FOR MINING IMAGE CLASSES
Author(s):      Zaher Al Aghbari , Rachid Sammouda , Jamal Abu Hassan
ISBN:      972-99353-6-X
Editors:      Nuno Guimarães and Pedro Isaías
Year:      2005
Edition:      1
Type:      Full Paper
First Page:      329
Last Page:      335
Language:      English
Cover:      cover          
Full Contents:      click to dowload Download
Paper Abstract:      In this paper, we demonstrate how semantic categories of images can be learnt from their color distributions using an effective probabilistic approach. Many previous probabilistic approaches are based on the Naïve Bayes that assume independence among attributes, which are represented by a single Gaussian distribution. We use a derivative of the Naïve Bayesian classifier, called Flexible Bayesian classifier, which abandon the normality assumption to better represent the image data. This approach is shown to yield high accuracy results on classifying image databases as compared to it counterpart the “Naïve Bayesian classifier” and the widely used K-Nearest Neighbor classifier.
   

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