Combining the contrast information with WLD for texture classification

A considerable amount of research work has been done for texture classification using local or global feature extraction methods. Inspired by Weber´s Law, a simple and robust Weber Local Descriptor (WLD) is a recently developed for local feature extraction. This WLD method did not consider the contrast information. In order to improve texture classification accuracy, we propose a hybrid approach that combines the WLD with contrast information in this paper. It utilizes the histogram of two complementary features WLD and the image variance calculated with the Probability Weighted Moments. Support vector machine is used for classification. The comparison of the proposed method with state of art methods like local binary pattern and WLD is experimental investigated on two publically available dataset, named as Brodatz and KTH-TIPS2-a. Results show that our proposed method outperforms over the state of art methods for texture classification.