Noise-Aware-Based Texture Descriptor, Evaluation Adjacent Distance Local Ternary Pattern EAdLTP for Image Classification

This study introduces a new local feature descriptor called evaluation window-based adjacent distance local ternary pattern EAdLTP for image classification. It is created by combining evaluation window EwLBP and adjacent distance local ternary pattern (AdLTP) to achieve robustness by encoding adjacent information. EwLBP produces an evaluation window to reduce noise in the neighbor’s values, and AdLTP captures the relationships between sequential neighbors. The adjacent sub-image window and the adjacent neighbor window are used to calculate the neighbors and extract the binary code that is modified to improve the information of the adjacent neighbors. The final EAdLTP pattern is divided into two parts (EAdLTPU and EAdLTPL), and the feature descriptor vector is obtained by concatenating their histograms. The proposed EAdLTP descriptor is tested on the KTH-TIPS and KTH-TIPS2b datasets and consistently outperforms other fundamental methods by being more robust against noise.