Rotation invariant texture classification using even symmetric Gabor filters

Abstract

Rotation invariant texture features are derived from the even symmetric Gabor filtered images of texture. The feature used is modified average absolute deviation from mean. Sixty Brodatz textures rotated in 12 different directions are classified using these features. Equal number of samples are used for training and testing phase. The percentage correct classification is 81.02. Segmentation of texture images with rotated textures is demonstrated using the features.