Local Adaptive Binary Patterns Using Diamond Sampling Structure for Texture Classification

Abstract

Local binary pattern (LBP) is sensitive to the noise and suffers from limited discriminative capability, and many LBP variants are reported in the recent literatures. Although a lot of significant progresses have been made, most LBP variants still have limitations of noise sensitivity, high dimensionality, and computational inefficiency. In view of this, we propose a new noise-robust local image descriptor named the diamond sampling structure-based local adaptive binary pattern (DLABP) in this letter, which aims at achieving both efficiency and simplicity at the same time. It mainly features three contributions: 1) an effective diamond sampling structure to decrease the feature dimensionality significantly by fixing the number of sampling neighbors to a constant of 8; 2) a simple and new “average method on the radial direction” to enhance the noise robustness; and 3) an effective adaptive quantization threshold strategy to restore the noise-corrupted nonuniform patterns back to possible uniform patterns. Extensive experiments are conducted on three benchmark texture databases of Outex, UIUC, and CUReT. Compared to state-of-the-art LBP-like methods, the proposed approach consistently demonstrates superior performances both in noise-free conditions and in the presence of high levels of noise, while it has a low complexity and a smaller feature dimension.