Image features Classification using Bayesian model.

This paper presents the first results of the Bayesian model in the problem of image classiffication using feature extraction. The proposed approach is similar to the well-known Markov processes and is based on conditional probabilities determined from the given data using the regression process with Kullback-Leibler divergence. In the contribution, we shortly review the complete process consisting of the Scale Invariant Feature Transform technique for the detection of key features, Bag of Visual Words, and classiffication. We compare the new methodology with the standard approach based on Support Vector Machine on a non-trivial benchmark containing real photographs of cats and dogs. [ABSTRACT FROM AUTHOR]