Rotation-invariant texture classification using circular Gabor wavelets

Abstract

Rotation-invariant texture classification is one of the most challenging problems in computer vision. We present a new and effective method for rotation-invariant texture classification based on circular Gabor wavelets. Two group features can be constructed by the mean and variance of the circular Gabor filtered images and rotation invariants. Using the two group features, a discriminant can be found to classify rotated images. The proposed method is evaluated on three public texture databases: Brodatz, CUReT, and UIUCTex. The experimental results, based on different testing data sets, show that the proposed method has comparatively high correct classification rates not only for the rotated images, but also for the images under different illuminations and viewing directions. The proposed method is robust to additive white noise.