Rotation-invariant texture classification using steerable Gabor filter bank

An efficient rotation invariant feature extraction technique for texture classification based on Gabor multi-channel filtering is proposed. In this technique, Gabor function is approximated by a set of steerable basis functions, which results in a significant saving in the computation cost. The classification of 15 classes of Brodatz textures are considered in our experiments. Results show that up to 40% of computation can be saved compared with traditional Gabor multi-channel filtering method. In the mean time, almost the same high texture classification correct rate can be achieved.