Application of a modified Inception-v3 model in the dynasty-based classification of ancient murals

EURASIP Journal on Advances in Signal Processing

Jianfang Cao¹  Yiming Jia²  Xiaodong Tian²  Minmin Yan²  Zibang Zhang² 

[1] Department of Computer Science & Technology, Xinzhou Teachers University, No. 10 Heping West Street, 034000, Xinzhou, China;School of Computer Science & Technology, Taiyuan University of Science and Technology, 030024, Taiyuan, China;School of Computer Science & Technology, Taiyuan University of Science and Technology, 030024, Taiyuan, China;

关键词: Mural classification;    Dynasty identification;    Transfer learning;    Inception-v3 model;   

DOI  :  10.1186/s13634-021-00740-8

来源: Springer

【 摘 要 】

It is difficult to identify the historical period in which some ancient murals were created because of damage due to artificial and/or natural factors; similarities in content, style, and color among murals; low image resolution; and other reasons. This study proposed a transfer learning-fused Inception-v3 model for dynasty-based classification. First, the model adopted Inception-v3 with frozen fully connected and softmax layers for pretraining over ImageNet. Second, the model fused Inception-v3 with transfer learning for parameter readjustment over small datasets. Third, the corresponding bottleneck files of the mural images were generated, and the deep-level features of the images were extracted. Fourth, the cross-entropy loss function was employed to calculate the loss value at each step of the training, and an algorithm for the adaptive learning rate on the stochastic gradient descent was applied to unify the learning rate. Finally, the updated softmax classifier was utilized for the dynasty-based classification of the images. On the constructed small datasets, the accuracy rate, recall rate, and F1 value of the proposed model were 88.4%, 88.36%, and 88.32%, respectively, which exhibited noticeable increases compared with those of typical deep learning models and modified convolutional neural networks. Comparisons of the classification outcomes for the mural dataset with those for other painting datasets and natural image datasets showed that the proposed model achieved stable classification outcomes with a powerful generalization capacity. The training time of the proposed model was only 0.7 s, and overfitting seldom occurred.

【 授权许可】

CC BY