Texture Image Recognition Based on Deep Convolutional Neural Network and Transfer Learning

Abstract

The traditional texture image recognition methods have a complex design process, and the existing methods based on deep learning can’t effectively solve the problem of insufficient texture image samples which lead to unsatisfying recognition accuracy. To solve the above problems, a texture image recognition method based on deep convolutional neural network and transfer learning is proposed. Firstly, a new transfer learning model is constructed by using the deep learning model pretrained on the large-scale ImageNet image dataset. Secondly, the reasonable model super-parameters are set, and the weighted sum of the training loss, the validation loss, and the deep feature distance between the training set and the validation set is taken as the cost function of training process. Finally, the best transfer learning model is determined by layer-by-layer training and validation. The experimental results show that the proposed method achieves 99.76%, 99.87%, 99.80%, 100.00% and 94.01% recognition accuracies on the CUReT, KTH-TIPS, UIUC, UMD and NewBarkTex texture datasets respectively, and has good robustness and recognition ability.