Texture classification using wavelet transform and support vector machines

Abstract

In this paper, we have investigated an approach based on support vector machines (SVMs) and wavelet transform (WT) for texture analysis. Texture analysis plays an important role in many tasks, ranging from remote sensing to medical imaging and query by content in large image databases. The main difficulty of texture analysis in the past was the lack of adequate tools to characterize different scales of texture effectively. The development in multi-resolution analysis such as wavelet transform has helped overcome this difficulty. It was found that the results using the combination of wavelet statistical and wavelet co-occurrence features generated from discrete wavelet transform for texture classification are promising. In recent years, support vector machines (SVM) have demonstrated excellent performance in a variety of pattern recognition problems. By applying SVM in tandem with the discrete wavelet transform for texture classification, it has produced more accurate classification results based on the Brodatz texture database