Performance analysis of texture classification techniques using MRMRF and WSFS & WCFS

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 analyses the performance of texture classification techniques using (i) multi resolution Markov random field (MRMRF) features and (ii) a combination of wavelet statistical features (WSFs) and wavelet co-occurrence features (WCFs) with two different texture datasets.