高技术通讯2024，Vol.34Issue(7)：692-704,13.DOI:10.3772/j.issn.1002-0470.2024.07.003
融合全局聚合与局部挖掘的建筑图像检索
Fusing global aggregation and local mining for architectural image retrieval
摘要
Abstract
To address the problem of low retrieval accuracy in architectural image retrieval due to scale variations and lo-cal occlusions,this paper proposes an architectural image retrieval network that integrates global aggregation and lo-cal mining.The method introduces global branch for multi-scale feature aggregation and a local branch for attention-guided feature mining following the ResNet50 backbone network.The network efficiently integrates complementary features from the two branches through an orthogonal fusion module.Specifically,the multi-scale feature aggrega-tion module utilizes mixed dilated convolutions and channel attention to adaptively aggregate globally different-scale targets,enhancing the network's ability to extract multi-scale salient features from architectural images.The atten-tion-guided feature mining module employs information complementary attention to mark and erase the most salient feature,achieving the mining of potential detail information in local regions.The proposed method achieves mean average precision(mAP)metrics of 81.54％(M)and 62.43％(H)on the ROxf dataset,as well as 90.28％(M)and 78.35％(H)on the RPar dataset,which are two major mainstream architectural datasets.Experimental results indicate that the method effectively overcomes the interference of scale variations and local occlusions,significantly improving the accuracy of architectural image retrieval.关键词
建筑图像/图像检索/特征聚合/特征挖掘Key words
architectural image/image retrieval/feature aggregation/feature mining引用本文复制引用
孟月波,张紫琴,刘光辉,徐胜军..融合全局聚合与局部挖掘的建筑图像检索[J].高技术通讯,2024,34(7):692-704,13.基金项目
陕西省重点研发计划(2021SF-429)和陕西省自然科学基础研究计划(2023-JC-YB-532)资助项目. （2021SF-429）
