Local directional ZigZag pattern: A rotation invariant descriptor for texture classification

摘要

Local feature descriptors play a key role in texture classification tasks. However, such traditional descriptors are deficient to capture the edges and orientations information and local intrinsic structure of images. This letter introduces a simple, new, yet powerful rotation invariant texture descriptor named Local Directional ZigZag Pattern (LDZP) by ZigZag scanning for effective representation of texture. Here at first we compute the directional edge information, so called local directional edge map (LDEM) of a texture image using the Kirsch compass mask in six different directions. Then Local ZigZag Pattern (LZP) is extracted from all LDEM images. Basically, the LZP characterizes the spatial ZigZag structure based on the relation between reference pixel and its adjacent neighboring pixels and is insensitive to the illumination changes. Finally, the uniform pattern histograms are computed from all directional LZP maps which are concatenated to form the final LDZP descriptor. Extensive experiments on texture classification shows the proposed LDZP descriptor achieves state-of-the-art performance in terms of average classification accuracy when applied to the large and well-known benchmark Outex database. We have also shown that LDZP descriptor is equally powerful for human face recognition. (C) 2018 Published by Elsevier B.V.