Local binary pattern (LBP) and its many variants have shown effectiveness for texture
classification. However, most of these LBP methods focus on encoding local intensity …

As one of the most successful local feature descriptors, the local binary pattern (LBP)
estimates the texture distribution rule of an image based on the signs of differences between …

Image texture description and analysis technology are the basis of many practical
applications in pattern recognition. This paper presents a novel and simple, yet powerful …

Texture recognition is one of the most important tasks in computer vision, with numerous
applications in several areas. Despite the recent success of end-to-end deep learning …

This paper presents a novel local image descriptor called Pattern of Local Gravitational
Force (PLGF). It is inspired by Law of Universal Gravitation. PLGF is a hybrid descriptor …

In this paper, we propose a novel descriptor called spatially weighted order binary pattern
(SWOBP) for color texture classification. The SWOBP descriptor not only encodes color …

The CNN framework has gained widespread attention in texture feature analysis; however,
handcrafted features still remain advantageous if computational cost needs to take …

Local binary pattern is one of the most known descriptors, which is used for texture
classification. Although completed local binary pattern is seemingly the most precise variant …

This paper proposes a novel feature representation method for color images, namely
quaternionic extended local binary pattern (QxLBP) with adaptive structural pyramid pooling …

Binary pattern family is considered as a powerful tool for visual texture classification. Most
popular methods improve the classification performance by multi-feature fusion. However …