Cited by 32Aesthetic Perception and AnalysisArt, Technology, and CultureImage Retrieval and Classification Techniques

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Compression ensembles quantify aesthetic complexity and the evolution of visual art

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Karjus, Andres, Canet Solà, Mar, Ohm, Tillmann, Ahnert, Sebastian E., Schich, Maximilian·2023·EPJ Data Science

Abstract

To the human eye, different images appear more or less complex, but capturing this intuition in a single aesthetic measure is considered hard. Here, we propose a computationally simple, transparent method for modeling aesthetic complexity as a multidimensional algorithmic phenomenon, which enables the systematic analysis of large image datasets. The approach captures visual family resemblance via a multitude of image transformations and subsequent compressions, yielding explainable embeddings. It aligns well with human judgments of visual complexity, and performs well in authorship and style recognition tasks. Showcasing the functionality, we apply the method to 125,000 artworks, recovering trends and revealing new insights regarding historical art, artistic careers over centuries, and emerging aesthetics in a contemporary NFT art market. Our approach, here applied to images but applicable more broadly, provides a new perspective to quantitative aesthetics, connoisseurship, multidimensional meaning spaces, and the study of cultural complexity.

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Research works printed but not linked: 9

  1. Bense M (1969) Einführung in Die Informationstheoretische Ästhetik Grundlegung Und Anwendung in Der Texttheorie. Rowolt Verlag, Reinbeks
  2. Galanter P (2003) What is generative art? Complexity theory as a context for art theory. In: GA2003–6th generative art conference. Citeseer
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  4. Rigau J, Feixas M, Sbert M (2007) Conceptualizing Birkhoff’s aesthetic measure using Shannon entropy and Kolmogorov complexity. In: Proceedings of the third eurographics conference on computational aesthetics in graphics, visualization and imaging. Computational Aesthetics’07. Eurographics Assoc., Goslar, pp 105–112
  5. Han SJ, Kelly P, Winters J, Kemp C (2021) Chinese characters have increased in visual complexity over three millennia. PsyArXiv preprint
  6. Taylor R (2004) Pollock, Mondrian and the nature: recent scientific investigations. Chaos Complex Letters 1(3):265–277
  7. Vasari G, Bondanella JC, Bondanella P (1998) The lives of the artists. Oxford University Press, Oxford
  8. Cassirer E (1927) Das Symbolproblem Und Seine Stellung Im System der Philosophie. Z Ästhet Allg Kunstwiss 21:295–322
  9. Srinivasa Desikan B, Evans J (2022) Aggregate, integrate and align to embed everything: a multi-modal framework for measuring cultural dynamics. In: Cultures in AI/AI in culture. A NeurIPS 2022 workshop

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Metadata

Authors
Karjus, A., Canet Solà, M., Ohm, T., Ahnert, S. E., & Schich, M.
Affiliations
  • Tallinn University (EE)
  • University of Cambridge (GB)
Corresponding author
Karjus, Andres
Venue
EPJ Data Science
Published
2023-06-14
Volume
12
Issue
1
ISSN
2193-1127 (ISSN-L)
OpenAlex
W4380685139
Cited by
32
References
85
Citations by year
2026: 8 · 2025: 12 · 2024: 7 · 2023: 3
FWCI
4.50
Type
journal article
Language
en
Open access
Locations
  • EPJ Data Science (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Horizon 2020 Framework Programme (810961)
  • Royal Society
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Aesthetic Perception and Analysis
  • Social Sciences › Arts and Humanities › Visual Arts and Performing Arts › Art, Technology, and Culture
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
Keywords
image compression, algorithmic complexity, visual arts, art style recognition, authorship attribution, image transformation, NFT art, art history, artistic career, cultural complexity

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