Texture classification using wavelet transform

Today, texture analysis plays an important role in many tasks, ranging from remote sensing to medical imaging and query by content in large image data bases. The main difficulty of texture analysis in the past was the lack of adequate tools to characterize different scales of textures effectively. The development in multi-resolution analysis such as Gabor and wavelet transform help to overcome this difficulty. This paper describes the texture classification using (i) wavelet statistical features, (ii) wavelet co-occurrence features and (iii) a combination of wavelet statistical features and cooccurrence features of one level wavelet transformed images with different feature databases. It is found that, the results of later method are promising. (C) 2002 Elsevier Science B.V. All rights reserved.