Classification of Ancient Buddhist Architecture in Multi-Cultural Context Based on Local Feature Learning

Abstract

Ancient Buddhist architecture plays an important role in the development of Chinese architectural culture. Under the background of multiculturalism, the ancient Buddhist architectural style has also been influenced to varying degrees. In order to realize automatic classification of ancient Buddhist architecture under multi-cultural background, this paper proposes an automatic classification algorithm based on local feature learning. Firstly, the ancient Buddhist architecture images are gridded, so that the backbone network can obtain relatively flat ancient Buddhist architecture image blocks. At the same time, the backbone network can learn more local details. Then, the grid reconstruction module is designed to strengthen the connection between the features of each block and highlight the distinguishing detail features. The accuracy of ancient Buddhist architecture classification can be effectively improved through image meshing and mesh reconstruction. Experiment and analysis are carried out by using the dataset of ancient Buddhist architecture images on the Internet. Experimental results show that the proposed algorithm has better recognition accuracy and robustness than other comparison algorithms.