Data augmentation techniques based on deep learning for Chinese paintings

Xinquan Luo; Qingchen Nie; Yinghui Wang; Zhuwen Zhao

doi:10.1117/12.3008104

14 November 2023 Data augmentation techniques based on deep learning for Chinese paintings

Xinquan Luo, Qingchen Nie, Yinghui Wang, Zhuwen Zhao

Proceedings Volume 12934, Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023); 129341V (2023) https://doi.org/10.1117/12.3008104
Event: 2023 3rd International Conference on Computer Graphics, Image and Virtualization (ICCGIV 2023), 2023, Nanjing, China

Abstract

Chinese paintings are generally divided into calligraphy, white drawing, and brushwork, often painted with ink, metallic pigments, and vegetable pigments using rice paper and cloth with distinct textures as physical carriers, and have a distinctive artistic style. In this paper, we propose a data augmentation method for Chinese-style paintings, which can better generate digital images that match the characteristics of Chinese-style paintings and are as semantically realistic as possible. First, we use SinGAN to train a single Chinese painting and generate 50 data augmentation results, which can reproduce the image texture and brush stroke style of a single Chinese painting. Subsequently, the Repaint model is used to semantically improve the data augmentation results to make them more realistic from a subjective perspective. Finally, we verify the effect of data augmentation in the image classification task based on VGG 16 and InceptionV3 and compare the effect of traditional data augmentation techniques with the deep-learning data augmentation technique proposed in this paper. The experimental results demonstrate that the training set processed by the deep-learning data augmentation technique can improve the prediction accuracy of the classification model, while the prediction accuracy of the classification model is improved again after training on the training set processed by the combination method of the traditional data augmentation technique and the deep-learning technique. This indicates that deep-learning data augmentation techniques can improve the efficiency of image tasks and avoid overfitting, which can be used in the study of the digitization of Chinese paintings.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xinquan Luo, Qingchen Nie, Yinghui Wang, and Zhuwen Zhao "Data augmentation techniques based on deep learning for Chinese paintings", Proc. SPIE 12934, Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023), 129341V (14 November 2023); https://doi.org/10.1117/12.3008104

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KEYWORDS

Data modeling

Education and training

Image processing

Neural networks

Denoising

Deep learning

Gallium nitride

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