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Research on the factors affecting accuracy of abstract painting orientation detection

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Abstract

An abstract painting’s hanging orientation directly affects how audiences judge its artistic value. Choosing the optimal hanging orientation can preserve the artist’s primary intention, preserving the original aesthetic value to a greater extent. Aesthetic value is frequently influenced by human subjective consciousness. Previous approaches improved direction recognition accuracy only by improving the feature extraction method and deep learning network. For this paper, the key factors that can influence recognition accuracy (such as painting content, image features and learning models) were investigated in conjunction with painting skills to find an experimental setting method that can enhance recognition accuracy. Experiment results show that the content of the painting has the greatest impact on classification accuracy. Furthermore, the average accuracy can be increased to more than 90% by reducing the number of painting categories in a dataset and the number of directions to be classified. While the outcome is superior to the state of the art, it is one-sided to rely solely on the information in the abstract painting. A combination of eye tracker data and questionnaires will be used in the future to examine the effect of audience subjective feelings on orientation classification.

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Data Availability

All abstract paintings in this study are available online at Wikiart (https://doi.org/http://www.wikiart.org) and Artsy (https://www.artsy.net/search?term=abstract). Machine learning algorithms in this study are available online at (https://github.com/scikit-learn). Analysis data in Tables 2 and 3 are available at (https://doi.org/10.1016/j.knosys.2021.107240). Other quantification data will be made available upon reasonable academic request within the limitations of informed consent by the corresponding author upon acceptance.

Notes

  1. http://www.wikiart.org

  2. https://www.artsy.net/search?term=abstract

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Acknowledgements

This work was supported in part by the project of the National Natural Science Foundation of China in 2022 under Grant 62102238, and in part by the project of the Natural Science Foundation of Shanxi Province in 2021 under Grant 20210302124555.

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Authors and Affiliations

  1. Institute of Electronic and Information Engineering, University of Electronic Science and Technology of China, Dongguan, China

    Qiang Zhao

  2. School of Automation and Software Engineering, Shanxi University, Taiyuan, China

    Qiang Zhao & Ziwen Wang

  3. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Zheng Chang

  4. Faculty of Information Technology, University of Jyväskylä, P.O. Box 35, FIN-40014, Jyväskylä, Finland

    Zheng Chang

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  1. Qiang Zhao
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Correspondence to Qiang Zhao.

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Zhao, Q., Chang, Z. & Wang, Z. Research on the factors affecting accuracy of abstract painting orientation detection. Multimed Tools Appl 82, 36231–36254 (2023). https://doi.org/10.1007/s11042-023-15034-4

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