Local ternary pattern based multi-directional guided mixed mask (MDGMM-LTP) for texture and material classification

Local feature descriptors (LFDs) have been researched and developed for decades, and their applications are still being examined nowadays. In addition, the fact that LFDs are significant in a variety of computer vision applications and have excellent performances that should be improved, especially in challenging conditions, is the key reason for the ongoing work in this area. Therefore, we design in this paper a novel model of local feature descriptor for texture representation. The essence of the proposed method, referred to as multi directional guided mixed mask based local ternary pattern (MDGMM-LTP), is to progressively extract comprehensive micro structure features by analyzing the differential excitation and directional information according to relationships between pixels sampled in a variety of spatial arrangements within each 3 × 3 neighborhood. The process of MDGMM-LTP consists first in constructing a new mixed hybrid directional kernel based on a combination of several edge directional kernels in eight directions, including Kirsch, Robinson, Prewitt and Frei–Chen masks and then both local and non-local pixel interactions are encoded according to a new compact pattern encoding scheme integrating both LTP’s and LDP’s concepts. MDGMM-LTP operator has good ability to capture stable and more discriminant micro structure information through complementary information resulting from both LTP’s and LDP’s encoders’ combination. With the help of the K-Nearest Neighbors (KNN) classifier, the experiments conducted on sixteen challenging texture datasets revealed that the proposed MDGMM-LTP descriptor outperforms the other commonly used LFDs-based and deep learning-based techniques in terms of overall performance. To further show the effectiveness of the proposed method, a statistical experiment applied over all the tested datasets through the Wilcoxon signed rank test is performed, which statistically validates the efficacy of the proposed method.