The importance of texture analysis and classification in image processing is well known. However, many existing texture classification schemes suffer from a number of drawbacks. A large number of features are commonly used to represent each texture and an excessively large image area is often required for the texture analysis, both leading to high computational complexity. Furthermore, most existing schemes are highly orientation dependent and thus cannot correctly classify textures after rotation. In this paper, two novel feature extraction techniques for rotation invariant texture classification are presented. These schemes, using the wavelet transform and Gaussian Markov random field modelling, are shown to give a consistently high performance for rotated textures in the presence of noise. Moreover, they use just four features to represent each texture and require only a 16/spl times/16 image area for their analysis leading to a significantly lower computational complexity than most existing schemes.
['Robert Porter', 'Nishan Canagarajah']
Robust rotation invariant texture classification