Texture classification by combining wavelet and contourlet features

In the recent decades, many features used to represent a texture were proposed. However, these features are always used exclusively. In this paper, a novel approach is presented that combines two types of features extracted by discrete wavelet transform and contourlet transform. Support vector machines (SVMs), which have demonstrated excellent performance in a variety of pattern recognition problems, are used as classifiers. The algorithm is tested on four different datasets, selected from Brodatz and VisTex database. The experimental results show that the combined features result in better classification rates than using only one type of those.