Multiresolution LDBP descriptors for texture classification using anisotropic diffusion with an application to wood texture analysis

In this paper, an efficient multiresolution method for texture classification based on anisotropic diffusion and local directional binary patterns (LDBP) is proposed. The method focuses on recognizing the most dominant LDBP descriptors that characterize the texture in an image by analyzing the effect of neighborhoods and radial distance of LDBP on texture classification. The texture descriptors are evaluated and compared on four texture datasets, namely, Brodatz, Oulu, VisTex and Kylberg. The discriminating power of multiresolution LDBP descriptors is assessed in classification experiments using k-NN classifier. The experimental results show that the proposed approach based on multiresolution LDBP descriptors and anisotropic diffusion yields better classification accuracy with low computational cost. The proposed method is then used in wood identification. The method is tested on fourteen texture images with varying color tones and number of samples per tone. The experimental results demonstrate the effectiveness of the proposed method in achieving the improved classification accuracy.