Rotation and scale invariant texture classification
Fernand S. Cohen, Z. Fan · 2003
The problem of classifying a textured image which might be subject to unknown rotation and magnification scale changes into one of C possible texture classes is discussed. The texture classes are modeled by Gaussian Markov random fields. A Bayes decision rule based on the generalized likelihood function is used to classify a given test sample. A maximum-likelihood estimate for the scale and rotation parameters for each of the C texture classes is computed under the assumption that the observed texture came from a particular unrotated and unscaled texture model. The test texture is allocated to the class with the highest generalized likelihood function. The classification power of the method is demonstrated through extensive experimental results on natural texture from the Brodatz album as well as for the problem of fabric inspection.>