A Genetic Programming Approach for Classification of Textures Based on Wavelet Analysis

In this paper, we propose a method for classifying textures using Genetic Programming (GP). Texture features are extracted from the energy of subimages of the wavelet decomposition. The GP is then used to evolve rules, which are arithmetic combinations of energy features, to identify whether a texture image belongs to certain class. Instead of using only one rule to discriminate the samples, a set of rules are used to perform the prediction by applying the majority voting technique. In our experiment results based on Brodatz dataset, the proposed method has achieved 99.6% test accuracy on an average. In addition, the experiment results also show that classification rules generated by this approach are robust to some noises on textures.