Texture classification using uniform rotation invariant gradient

In this paper, we present a novel descriptor called uniform rotation invariant gradient(URIG) aiming at texture classification under variant rotation and illumination condition. Instead of using URIG directly, a 2D descriptor can be formulated combining URIG with average of local pixels. Given a texture image, such 2D descriptors are extracted from every pixel followed by clustering. The centers of clustering can be viewed as a texton dictionary over which a histogram is computed as the representation of given texture image. Experiments are carried out on Outex and CUReT databases comparing to state-of-the-art approaches. Our proposed method achieved promising performance against illumination and rotation changes with least cost for representing histogram dimension.