Local Adaptive Binary Patterns Using Diamond Sampling Structure for Texture Classification

• Published in: IEEE signal processing letters Vol. 24; no. 6; pp. 828 - 832
• Main Authors: Pan, Zhibin, Wu, Xiuquan, Li, Zhengyi, Zhou, Zhili
• Format: Journal Article
• Language: English
• Published: IEEE 01.06.2017
• Subjects: Complexity theory; Diamond; Diamond sampling structure; Feature extraction; local adaptive threshold; local binary pattern (LBP); low complexity; Noise robustness; Quantization (signal); Robustness; texture classification
• ISSN: 1070-9908; 1558-2361
• DOI: 10.1109/LSP.2017.2694460

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.