计算机科学与探索2025，Vol.19Issue(5)：1280-1294,15.DOI:10.3778/j.issn.1673-9418.2404027
背景支持下的全域特征响应图像分类网络
Background-Supported Global Feature Response Image Classification Network
摘要
Abstract
The lack of background information support in current image classification methods leads to the limited classifi-cation accuracy of the model.Aiming at this problem,a background-supported global feature response image classification network(BGRNet)is proposed.Firstly,based on WRN(wide residual networks)residual networks,a new background-supported activation function BS(background-supported)is proposed,which introduces a background support mechanism through the BS activation function,so that the network can focus on the background information smoothly while focusing on the foreground information of the target.Then,a full-domain feature response module BGR(background-supported global feature response)is proposed,and BGR is embedded into the residual branch to restore the image full domain fea-tures,which reduces the loss of feature information due to the convolution operation to a certain extent.Finally,this paper adjusts the internal network structure of the residual block by adjusting the activation function,the forward propagation order of batch normalization and removing Dropout(Dropout Regularization),amplifying the background support role of the BS activation function to the overall network model,and promoting the effective transmission of background information in the network.By introducing the background information support mechanism,BGRNet not only considers the support role of the target foreground information in the process of image classification,but also considers the support role of the background information in the classification process,which effectively improves the network training efficiency while im-proving the network classification accuracy.Experimental results on FashionMNIST,KMNIST,CIFAR-10,CIFAR-100 and SVHN datasets show that BGRNet significantly improves the classification performance of the baseline model,and compared with the current mainstream methods,BGRNet has higher classification accuracy and stronger generalization performance.关键词
图像分类/背景支持/全域特征响应/特征还原/残差网络Key words
image classification/background support/full domain feature response/feature restoration/residual network分类
信息技术与安全科学引用本文复制引用
姜文涛,李威达,张晟翀..背景支持下的全域特征响应图像分类网络[J].计算机科学与探索,2025,19(5):1280-1294,15.基金项目
国家自然科学基金(61601213) （61601213）
辽宁省自然科学基金(20170540426) （20170540426）
辽宁省教育厅重点基金(LJYL049). This work was supported by the National Natural Science Foundation of China(61601213),the Natural Science Foundation of Liaoning Province(20170540426),and the Foundation of Liaoning Provincial Education Department(LJYL049). （LJYL049）
