Scale Selective Extended Local Binary Pattern for Texture Classification

Abstract

In this paper, we propose a new texture descriptor, scale selective extended local binary pattern (SSELBP), to characterize texture images with scale variations. We first utilize multi-scale extended local binary patterns (ELBP) with rotation-invariant and uniform mappings to capture robust local micro- and macro-features. Then, we build a scale space using Gaussian filters and calculate the histogram of multi-scale ELBPs for the image at each scale. Finally, we select the maximum values from the corresponding bins of multi-scale ELBP histograms at different scales as scale-invariant features. A comprehensive evaluation on public texture databases (KTH-TIPS and UMD) shows that the proposed SSELBP has high accuracy comparable to state-of-the-art texture descriptors on gray-scale-, rotation-, and scale-invariant texture classification but uses only one-third of the feature dimension.

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017

References in corpus (2)

• Two decades of local binary patterns: A survey
• Completed Local Derivative Pattern for Rotation Invariant Texture Classification

Cited by in corpus (2)

• Texture Classification using Block Intensity and Gradient Difference (BIGD) Descriptor
• Texture retrieval using periodically extended and adaptive curvelets