Research on Dynasty Classification of Dunhuang Murals Based on Convolutional Neural Networks

Abstract

Dunhuang Murals are one of China's intangible cultural heritages, which have received extensive attention. Identifying and classifying the dynasties of ancient murals quickly and accurately is extremely important for the study of Dunhuang Murals and the digital protection and inheritance. A method to classify Dunhuang Murals dynasties based on convolutional neural network (CNN) is proposed in this paper. First, the Dunhuang Murals data set is constructed from the mural materials; then, four convolutional neural network models with different depths and structures are constructed and trained; finally, the network model is tested using the test set and an appropriate classification model is selected. The experimental results show that for the Dunhuang mural image data sets of five different dynasties in this paper,the four models obtain high classification accuracy, and the accuracy of VGG11 and VGG19 reach 96%. Among them,the classification accuracy of murals in the Sui Dynasty and the Five Dynasties and Song Dynasties is lower than that of other dynasties in this paper.