Extracting discriminative regions in images plays a crucial role in fine-grained image classification.Existing fine-grained image classification methods ignore the multi-scale information of the image and the interaction of adjacent spatial posi-tion information,and it is difficult to accurately extract subtle features.Moreover,the traditional CNN method is insufficient to capture long-distance semantic information and cannot obtain accurate global information.To address these issues,a fine-grained classification algorithm based on Res2Net and recursive gated convolution module is designed.In this network,the weakly supervised data augmentation network(WS-DAN)is used for data expansion to prevent overfitting,and Res2Net is used as a feature extraction network,which can extract image information of different scales,increase the receptive field of network layer.Meanwhile,a recursive gated convolution module is introduced into the network to further fuse information and realize high-order feature interaction to improve network modeling capabilities.The proposed method achieves 90.36%,93.1%and 94.3%accuracy on the three public datasets of CUB-200-2011,Stanford Dogs and FGVC-Aircraft,respectively,which can ef-fectively extract subtle features of images and achieve classification.
关键词
深度学习/细粒度分类/Res2Net/递归门控卷积
Key words
deep learning/fine-grained classification/Res2Net/recursive gated convolution
维普
万方数据