Affiliations: [a] School of Information Engineering, Beijing Institute of Graphic Communication, Beijing, China
| [b] School of New Media, Beijing Institute of Graphic Communication, Beijing, China
Correspondence:
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Corresponding author. Hongjuan Wang, School of New Media, Beijing Institute of Graphic Communication, Beijing, China. E-mail: [email protected].
Abstract: Traditional Chinese painting (TCP), culturally significant, reflects China’s rich history and aesthetics. In recent years, TCP classification has shown impressive performance, but obtaining accurate annotations for these tasks is time-consuming and expensive, involving professional art experts. To address this challenge, we present a semi-supervised learning (SSL) method for traditional painting classification, achieving exceptional results even with a limited number of labels. To improve global representation learning, we employ the self-attention-based MobileVit model as the backbone network. Furthermore, We present a data augmentation strategy, Random Brushwork Augment (RBA), which integrates brushwork to enhance the performance. Comparative experiments confirm the effectiveness of TCP-RBA in Chinese painting classification, demonstrating outstanding accuracy of 88.27% on the test dataset, even with only 10 labels, each representing a single class.
Keywords: Traditional chinese paintings, brushwork, semi-supervised learning, image classification
