Extraction of textural features using anisotropic diffusion and local directional binary patterns

Abstract

Texture classification is an important approach for effective classification of digital images. Extraction of features is a challenging task due to spatial entanglement, orientation mixing and high frequency overlapping. The partial differential equation (PDE) transform is an efficient method for functional mode decomposition. This paper presents a novel approach for texture modeling based on PDE. Each image f is decomposed into a family of derived sub-images. f is split into u component, obtained with anisotropic diffusion and the texture component v, which is calculated by difference between original image and the u component. The feature set is obtained by applying local directional binary patterns (LDBP) approach and extracting co-occurrence parameters. The separability of texture classes is enhanced using linear discriminant analysis (LDA). The features obtained from LDA are class representatives. The proposed approach is validated on sixteen Brodatz textures. The k-NN classifier is used for classification. The experimental results indicate that the proposed approach leads to significant improvements in classification accuracy, reduction in feature dimensionality, reduction in computational and time complexity. © Technomathematics Research Foundation.