Extracting effective features is always a challenging problem for texture classification
because of the uncertainty of scales and the clutter of textural patterns. For texture …

Current deep learning-based texture recognition methods extract spatial orderless features
from pre-trained deep learning models that are trained on large-scale image datasets …

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 …

Local binary pattern (LBP) and its variants have been successfully applied in texture feature
extraction. However, it is hard for most LBP-based methods to effectively describe and …

Aiming at the defect of Local Binary Pattern (LBP) and its variants, this paper presents a new
modeling of the conventional LBP operator for texture classification. Named Attractive-and …

Automatic facial expression recognition (FER) has been extensively studied owing to its
wide range of applications, such as in e-learning platforms used to automatically collect the …

In this work, we present an ensemble of descriptors for the classification of virus images
acquired using transmission electron microscopy. We trained multiple support vector …

Convolutional neural networks have shown successful results in image classification
achieving real-time results superior to the human level. However, texture images still pose …

In this paper, we propose a new hybrid Local Binary Pattern (LBP) based on Hessian matrix
and Attractive Center-Symmetric LBP (ACS-LBP), called Hess-ACS-LBP. d The Hessian …

The quality control of steel products' surface is of utmost importance, where several
inspection techniques and technologies have been proposed over the last few years …