Texture Classification using Naïve Bayes Classifier

Summary This paper presents a texture classification algorithm using Independent Component Analysis and Naïve Bayes Classifier. Naïve Bayes is one of the most effective and efficient classification algorithms. Naïve Bayes classifiers still tend to perform very well under unrealistic assumption. Especially for small sample sizes, naive Bayes classifiers can outperform the more powerful classifiers. Texture features are extracted using Independent Component Analysis and then classified by Naïve Bayes Classifier. Experiments were performed in order to evaluate the performance of the proposed classifier. It consists of texture images from the Describable Textures Dataset (DTD) and Brodatz album. Experimental results show that the proposed algorithm has an encouraging performance. The Naïve Bayes Classifier produces a very accurate classification results.

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