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TSC-Net: Theme-Style-Color Guided Artistic Image Aesthetics Assessment Network

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Advances in Computer Graphics (CGI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14495))

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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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Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Tiangong University, No. 399 Binshui Road, Tianjin, 300387, China

    Yin Wang, Wenjing Cao, Nan Sheng, Huiying Shi, Congwei Guo & Yongzhen Ke

  2. Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, Tianjin, 300387, China

    Yongzhen Ke

Authors
  1. Yin Wang
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  2. Wenjing Cao
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  3. Nan Sheng
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  4. Huiying Shi
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  5. Congwei Guo
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  6. Yongzhen Ke
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Corresponding author

Correspondence to Yongzhen Ke .

Editor information

Editors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Bin Sheng

  2. Shanghai Jiao Tong University, Shanghai, China

    Lei Bi

  3. University of Sydney, Sydney, NSW, Australia

    Jinman Kim

  4. MIRALab-CUI, University of Geneve, Carouge, Geneve, Switzerland

    Nadia Magnenat-Thalmann

  5. Swiss Federal Institute of Technology, Lausanne, Switzerland

    Daniel Thalmann

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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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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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  • DOI: https://doi.org/10.1007/978-3-031-50069-5_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50068-8

  • Online ISBN: 978-3-031-50069-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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