Image Texture Recognition Algorithm Based on Batch Normalization and Attention Mechanism
HE Zehua
QIAO Yansong
ZHAO Xuying
ZHAO Geng
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.
Keywords：image texturefeature extractionconvolutional neural networkbatch normalizationattention mechanism
Publication Date：2024-03-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 646-652 )