Two decades of local binary patterns: A survey
It provides a comprehensive overview for researchers and practitioners in computer vision and image processing, but is incremental as it summarizes existing work rather than introducing new methods.
This survey reviews the development of local binary patterns (LBP) over two decades, highlighting their evolution into discriminative and efficient texture descriptors that have bridged the gap between texture and generic image/video analysis, with numerous variants improving robustness and applicability across domains like 2D, spatiotemporal, 3D, 4D, and 1D signals.
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.