Dynasty recognition algorithm of an adaptive enhancement capsule network for ancient mural images

Abstract

Abstract In view of the polysemy of mural images and the style difference among mural images painted in different dynasties as well as the high energy costs of the traditional manual dynasty classification method, which resorts to mural texts and historical documents, this study proposed an adaptive enhancement capsule network (AECN) for automatic dynasty identification of mural images. Based on the original capsule network, we introduced a preconvolution structure to extract the high-level features of the mural images from Mogao Grottoes, such as color and texture. Then, we added an even activation operation to the layers of the network to enhance the fitting performance of the model. Finally, we performed adaptive modifications on the capsule network to increase the gradient smoothness of the model, based on which to optimize the model and thus to increase its classification precision. With the self-constructed DH1926 data set as the study subject, the proposed model achieved an accuracy of 84.44%, an average precision of 82.36%, an average recall rate of 83.75% and a comprehensive assessment score F1 of 83.96%. Compared with modified convolution neural networks and the original capsule network, the model proposed in study increased all the considered indices by more than 3%. It has a satisfactory fitting performance, which can extract the rich features of mural images at multiple levels and well express their semantic information. Furthermore, it has a higher accuracy and better robustness in the classification of the Mogao Grottoes murals, and therefore is of certain application values and research significance.

Keywords

• Preconvolution
• Even layer activation
• Adaptive enhancement
• Capsule network
• Mural dynasty classification