Directional Neighborhood Topologies Based Multi-Scale Quinary Pattern for Texture Classification

This paper ideates a new computationally simple and effective local image feature descriptor, referred to as Directional Neighborhood Topologies based Multi-scale Quinary Pattern (DNT-MQP) for texture description and classification. The essence of DNT-MQP is to encode the structure of local neighborhood by analyzing the differential excitation and directional information using various directional neighborhood topologies and new pattern encoding scheme. We first designed four different versions of single scale DNT-QP features based on four directional neighborhood topologies based sampling sets which are then combined together to build the effective multiscale DNT-MQP model. Unlike some existing parametric methods that employ static thresholds to perform thresholding, the construction process of DNT-MQP includes an automatic mechanism for dynamic thresholds estimation. Thanks to a richer local description ensured by exploiting complementary information resulting from single scale DNT-QP operators' combination, DNT-MQP descriptor has high capability to elicit stable and discriminative feature representation than other local feature descriptors. In addition, DNT-QP has the advantages of computational simplicity in feature extraction and low-dimensionality in feature representation. The effectiveness of DNT-QP is evaluated on sixteen challenging texture datasets and it is found that it maintains a high level of performance stability where the achieved performances are competitive or better than several recent most promising state-of-the-art texture descriptors as well as deep learning-based feature extraction approaches. Impressively, DNT-MQP showed good tolerance to rotation as well as illumination, scale and viewpoint changes against certain descriptors which are originally conceived to deal with these challenges. Furthermore, statistical hypothesis testing through Wilcoxon signed rank test is applied to prove the statistical significance of the accuracy improvement obtained in all the datasets.