# Two decades of local binary patterns: A survey

arXiv: https://arxiv.org/abs/1612.06795 · HTML: https://mlsanity.com/paper/1612.06795/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2016.

## Built on by (most cited first, 6 of 6)

- [PCQA-GRAPHPOINT: Efficients Deep-Based Graph Metric For Point Cloud Quality Assessment](https://mlsanity.com/paper/2211.02459/) (2022)
- [From Keypoints to Object Landmarks via Self-Training Correspondence: A novel approach to Unsupervised Landmark Discovery](https://mlsanity.com/paper/2205.15895/) (2022)
- [Quality evaluation of point clouds: a novel no-reference approach using transformer-based architecture](https://mlsanity.com/paper/2303.08634/) (2023)
- [MV-MR: multi-views and multi-representations for self-supervised learning and knowledge distillation](https://mlsanity.com/paper/2303.12130/) (2023)
- [Deep Neural Networks Fused with Textures for Image Classification](https://mlsanity.com/paper/2308.01813/) (2023)
- [Uncertainty-Aware Decomposed Hybrid Networks](https://mlsanity.com/paper/2503.19096/) (2025)

## Abstract (arXiv)

Texture is an important characteristic for many types of images. In recent years very discriminative and computationally efficient local texture descriptors based on local binary patterns (LBP) have been developed, which has led to significant progress in applying texture methods to different problems and applications. Due to this progress, the division between texture descriptors and more generic image or video descriptors has been disappearing. A large number of different variants of LBP have been developed to improve its robustness, and to increase its discriminative power and applicability to different types of problems. In this chapter, the most recent and important variants of LBP in 2D, spatiotemporal, 3D, and 4D domains are surveyed. Interesting new developments of LBP in 1D signal analysis are also considered. Finally, some future challenges for research are presented.