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.
KeywordsSupport Vector Machine Filter Bank Texture Classification Gabor Filter Combine Feature
- 1.Gibson, J.J.: The Perception of The Visual World. Riverside Press, Cambridge (1950)Google Scholar
- 9.Do, M.N., Vetterli, M.: Contourlets: A Directional Multiresolution Image Representation.In: Proc. of IEEE International Conference on Image Processing, Rochester (September 2002)Google Scholar
- 10.Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)Google Scholar
- 11.Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)Google Scholar