基于深度卷积神经网络和迁移学习的纹理图像识别

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

针对传统的纹理图像识别方法设计过程复杂,而现有的基于深度学习的方法未能有效解决纹理图像样本数量偏少而导致识别精度不佳的问题,提出一种基于深度卷积神经网络和迁移学习的纹理图像识别方法.首先利用在大规模ImageNet图像数据集上预先训练的深度学习模型构造新的迁移学习模型;然后设置合理的模型超参数,并将训练损失、验证损失以及训练集和验证集深度特征距离的加权和作为训练的代价函数;最后通过逐层训练和验证确定最佳的迁移学习模型.实验结果表明,所提方法在CUReT,KTH-TIPS,UIUC,UMD和NewBarkTex纹理数据库上分别取得了99.76%,99.87%,99.80%,100.00%和94.01%的识别精度,具有良好的稳健性和识别能力.

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.