# Scale Selective Extended Local Binary Pattern for Texture Classification

## TL;DR

This paper introduces SSELBP, a novel texture descriptor that effectively captures scale-invariant features in textures with reduced feature dimension, demonstrating high accuracy on public databases.

## Contribution

The paper proposes a new scale selective extended local binary pattern (SSELBP) that improves texture classification by capturing scale-invariant features with fewer features.

## Key findings

- Achieves high accuracy comparable to state-of-the-art methods.
- Uses only one-third of the feature dimension.
- Performs well on gray-scale, rotation-, and scale-invariant textures.

## 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.