东华大学学报（英文版）2024，Vol.41Issue(2)：115-129,15.DOI:10.19884/j.1672-5220.202303004
基于细粒度特征的面料图像检索
Fabric Image Retrieval Based on Fine-Grained Features
摘要
Abstract
Fabric image retrieval is crucial for textile mills to manage their inventory and samples,but it is challenging due to the diverse appearance and fine-grained texture of fabrics.This paper proposes an algorithm based on fine-grained features to deal with this issue.The algorithm uses the coordinate attention(CA)module to extract precise location information of the fabric images and scales the overall network structure of MobileNetV3 to reduce the training time and model parameters.The optimized model is selected based on the scaling factor method,and fabric retrieval experiments are conducted on the fabric image dataset(FID).The results show that the algorithm effectively improves the accuracy of fabric image feature extraction,with a retrieval accuracy(Acc)of 91.82%and floating point operations(FLOPs)of 175.34 MB.The Acc is improved by 13.49 percentage points compared with that of the original MobileNetV3 model,while the training time is reduced,and the inference speed is improved by 25.14%.The algorithm has practical application value.关键词
面料图像检索/MobileNetV3/细粒度特征/注意力机制/缩放系数Key words
fabric image retrieval/MobileNetV3/fine-grained feature/attention mechanism/scaling factor分类
信息技术与安全科学引用本文复制引用
罗辛,夏冬梅,陶然,史有群..基于细粒度特征的面料图像检索[J].东华大学学报（英文版）,2024,41(2):115-129,15.基金项目
National Key Research and Development Program of China(No.2020YFB1707700) （No.2020YFB1707700）
