Bayesian based classifier for mining image classes

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.