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Combining Generative And Discriminative Classifiers For Semantic Automatic Image Annotation
Brahim MINAOUI, Mustapha OUJAOURA, Mohammed FAKIR
Pages - 225 - 244     |    Revised - 10-07-2014     |    Published - 10-08-2014
Volume - 8   Issue - 5    |    Publication Date - September / October 2014  Table of Contents
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KEYWORDS
Automatic Image Annotation, Discriminative Classifier, Generative Classifier, Neural Networks, Bayesian Networks.
ABSTRACT
The object image annotation problem is basically a classification problem and there are many different modeling approaches for the solution. These approaches can be classified into two main categories such as generative and discriminative. An ideal classifier should combine these two complementary approaches. In this paper, we present a method achieving this combination by using the discriminative power of the neural networks and the generative nature of Bayesian networks. The evaluation of the proposed method on three typical image’s database has shown some success in automatic image annotation.
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1 Minaoui, B., Oujaoura, M., Fakir, M., & Sajieddine, M. (2015). Toward an Effective Combination of multiple Visual Features for Semantic Image Annotation. TELKOMNIKA Indonesian Journal of Electrical Engineering, 15(3), 533-543.
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Dr. Brahim MINAOUI
Faculty of Science and Technology, Computer Science Department, Sultan Moulay Slimane University. PO Box. 523, Béni Mellal, Morocco. - Morocco
Dr. Mustapha OUJAOURA
Faculty of Science and Technology, Computer Science Department, Sultan Moulay Slimane University. PO Box. 523, Béni Mellal, Morocco - Morocco
M.Mustapha.Oujaoura@ieee.org
Dr. Mohammed FAKIR
Faculty of Science and Technology, Computer Science Department, Sultan Moulay Slimane University. PO Box. 523, Béni Mellal, Morocco. - Morocco