Texture Classification Using Deep Learning and Enhanced Local Binary Pattern

Image texture classification is crucial in machine vision and image processing. The primary step in this classification process involves extracting features from the image. Numerous techniques have been developed for feature extraction from textured images; however, local binary patterns (LBP), in both their original and enhanced forms, stand out due to their simplicity in implementation and their ability to extract effective features that yield high classification accuracy. Experts widely recognize that deep neural networks excel at classifying images and extracting detailed features. Building on the strengths of these methods, this study introduces a new model that merges deep learning with enhanced local binary patterns. This model has been tested on the Outex and CUReT datasets. The experimental results indicate that combining deep learning with an improved local binary pattern method significantly enhances the accuracy of texture image classification. According to these results, the classification accuracy of this new model surpasses that of previous methods.

کلیدواژه ها:

Classification ، deep learning ، Texture images ، Local binary pattern ، Enhanced local binary pattern