Abstract
Image aesthetic assessment is a hot issue in current research, but less research has been done in the art image aesthetic assessment field, mainly due to the lack of large-scale artwork datasets. The recently proposed BAID dataset fills this gap and allows us to delve into the aesthetic assessment methods of artworks, and this research will contribute to the study of artworks and can also be applied to real-life scenarios, such as art exams, to assist in judging. In this paper, we propose a new method, TSC-Net (Theme-Style-Color guided Artistic Image Aesthetics Assessment Network), which extracts image theme information, image style information, and color information and fuses general aesthetic information to assess art images. Experiments show that our proposed method outperforms existing methods using the BAID dataset.
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Wang, Y., Cao, W., Sheng, N., Shi, H., Guo, C., Ke, Y. (2024). TSC-Net: Theme-Style-Color Guided Artistic Image Aesthetics Assessment Network. In: Sheng, B., Bi, L., Kim, J., Magnenat-Thalmann, N., Thalmann, D. (eds) Advances in Computer Graphics. CGI 2023. Lecture Notes in Computer Science, vol 14495. Springer, Cham. https://doi.org/10.1007/978-3-031-50069-5_17
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