Texture classification using improved ResNet based on multi-scale attention mechanism
Qiu Lu, Haotian Chen, Tiejun Yang · 2021
Aiming at the problem of inaccurate classification of textures at different scales in traditional texture classification methods, this paper proposed a deep convolutional neural network (CNN) based on improved residual blocks to increase the accuracy of texture classification. First, the two convolution layers in the original residual block were replaced with two dilated convolution layers, and a spatial attention module after the second dilated convolution layers was inserted. Thereafter, the residual connections were used for feature fusion to obtain a greater receptive field and attention-enhanced features. Second, based on the improved residual blocks, a multi-scale texture classification CNN was stacked in a way of increasing the number of block channels. The experiment was performed on a 64-class texture dataset. Experiments show that, compared with the state-of-the-art methods, the proposed method achieved a higher classification accuracy of 99.17%.