计算机与数字工程2024，Vol.52Issue(3)：646-652,7.DOI:10.3969/j.issn.1672-9722.2024.03.002
基于批归一化与注意力机制的图像纹理识别算法
Image Texture Recognition Algorithm Based on Batch Normalization and Attention Mechanism
摘要
Abstract
Aiming at the cumbersome feature extraction of traditional image texture recognition methods,poor results,high in-ter-class ambiguity of texture,and low intra-class discrimination,a convolutional network image texture recognition algorithm based on batch normalization and attention mechanism is proposed.Through layer-by-layer batch normalization,the scattered data is unified,and the loss oscillation and gradient disappearing problems of the optimization algorithm are optimized.The key areas of the image and the key features of the texture are highlighted through the attention mechanism of the channel domain and the space domain.The experimental results show that the proposed algorithm model has low parameters and fast calculation speed.The recogni-tion rate on the dataset is 99.84%,surpassing the benchmark model and other network models,it proves that the algorithm has good recognition effect on image texture.关键词
图像纹理/特征提取/卷积神经网络/批归一化/注意力机制Key words
image texture/feature extraction/convolutional neural network/batch normalization/attention mechanism分类
信息技术与安全科学引用本文复制引用
贺泽华,乔延松,赵绪营,赵耿..基于批归一化与注意力机制的图像纹理识别算法[J].计算机与数字工程,2024,52(3):646-652,7.基金项目
北京电子科技学院"一流学科"建设项目(编号:1101014) （编号:1101014）
国家自然科学基金项目(编号:61772047)资助. （编号:61772047）
