Background-Supported Global Feature Response Image Classification Network

The lack of background information support in current image classification methods leads to the limited classification 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 features, 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 improving 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.