Ms. Qingchen Nie Profile

Qingchen Nie

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Proceedings Article | 14 November 2023 Paper

Target segmentation algorithm for Chinese traditional flower-and-bird paintings based on object detection

Xinquan Luo, Yinghui Wang, Zhuwen Zhao, Qingchen Nie

Proceedings Volume 12934, 129341U (2023) https://doi.org/10.1117/12.3008107

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Recognizing and segmenting artistic targets in Chinese paintings is an important method for analyzing and studying this art form. In order to enrich the expressive forms and cultural connotations of Chinese paintings, as well as promote the modernization of traditional culture, this paper proposes a segmentation method for animals in Chinese paintings. Firstly, using the Swin Transformer, artistic targets such as animals in Chinese paintings are detected, and the interested target image blocks are cropped. Then, the Attention UNet model is employed to achieve high-precision image segmentation for animals in Chinese paintings. Experimental results demonstrate that our algorithm successfully segments 19 species of animals in the sample dataset, achieving high accuracy and accurately segmenting the artistic targets in Chinese traditional flower-and-bird paintings. The achievements of this paper can be applied to the digital research of Chinese paintings, providing technical references for the inheritance and development of Chinese traditional painting.

Proceedings Article | 14 November 2023 Paper

Data augmentation techniques based on deep learning for Chinese paintings

Xinquan Luo, Qingchen Nie, Yinghui Wang, Zhuwen Zhao

Proceedings Volume 12934, 129341V (2023) https://doi.org/10.1117/12.3008104

KEYWORDS: Data modeling, Education and training, Image processing, Neural networks, Denoising, Gallium nitride, Deep learning, Statistical modeling, Diffusion, Reverse modeling

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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.

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