<title>Rotation invariant texture classification using multichannel filtering</title>

Abstract

Rotational invariant texture classification is required for many real world applications. Rotation invariant texture features are derived from the even symmetric Gabor filtered images of texture. The feature used is ADD from mean. It can be shown that rotation of input image is equivalent to a translation of the channel output along the orientation axis. This property is exploited to convert rational variant features to rotational invariant features. Discrete Fourier Transform of the feature is taken in rogation dimension to make the feature ration invariant. The classification of 45 Brodatz textures rotated in 12 different directions is done using these features. The number of samples used for training and testing phase are 4320. The percentage correct classification is 85.25.