Local Adaptive Binary Patterns Using Diamond Sampling Structure for Texture Classification

Zhibin Pan

doi:10.1109/LSP.2017.2694460

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Local Adaptive Binary Patterns Using Diamond Sampling Structure for Texture Classification

2017-06-01

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Zhibin Pan *

Zhengyi Li

周支立 (Zhili Zhou)

DOI：10.1109/LSP.2017.2694460

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Abstract

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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.

Keywords:

Diamond sampling structure

local adaptive threshold

local binary pattern (LBP)

low complexity

texture classification