Compression ensembles quantify aesthetic complexity and the evolution of visual art
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2023-06-01
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| Series: | EPJ Data Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1140/epjds/s13688-023-00397-3 |
| _version_ | 1797801521629364224 |
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| author | Andres Karjus Mar Canet Solà Tillmann Ohm Sebastian E. Ahnert Maximilian Schich |
| author_facet | Andres Karjus Mar Canet Solà Tillmann Ohm Sebastian E. Ahnert Maximilian Schich |
| author_sort | Andres Karjus |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T04:51:44Z |
| format | Article |
| id | doaj.art-a42db8de5af54a9f9d8134c989b7e763 |
| institution | Directory Open Access Journal |
| issn | 2193-1127 |
| language | English |
| last_indexed | 2024-03-13T04:51:44Z |
| publishDate | 2023-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EPJ Data Science |
| spelling | doaj.art-a42db8de5af54a9f9d8134c989b7e7632023-06-18T11:09:22ZengSpringerOpenEPJ Data Science2193-11272023-06-0112112310.1140/epjds/s13688-023-00397-3Compression ensembles quantify aesthetic complexity and the evolution of visual artAndres Karjus0Mar Canet Solà1Tillmann Ohm2Sebastian E. Ahnert3Maximilian Schich4ERA Chair for Cultural Data Analytics, Tallinn UniversityERA Chair for Cultural Data Analytics, Tallinn UniversityERA Chair for Cultural Data Analytics, Tallinn UniversityDepartment of Chemical Engineering and Biotechnology, University of CambridgeERA Chair for Cultural Data Analytics, Tallinn UniversityAbstract 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.https://doi.org/10.1140/epjds/s13688-023-00397-3Aesthetic complexityKolmogorov complexityImage compressionArt historyFamily resemblanceArtistic careers |
| spellingShingle | Andres Karjus Mar Canet Solà Tillmann Ohm Sebastian E. Ahnert Maximilian Schich Compression ensembles quantify aesthetic complexity and the evolution of visual art EPJ Data Science Aesthetic complexity Kolmogorov complexity Image compression Art history Family resemblance Artistic careers |
| title | Compression ensembles quantify aesthetic complexity and the evolution of visual art |
| title_full | Compression ensembles quantify aesthetic complexity and the evolution of visual art |
| title_fullStr | Compression ensembles quantify aesthetic complexity and the evolution of visual art |
| title_full_unstemmed | Compression ensembles quantify aesthetic complexity and the evolution of visual art |
| title_short | Compression ensembles quantify aesthetic complexity and the evolution of visual art |
| title_sort | compression ensembles quantify aesthetic complexity and the evolution of visual art |
| topic | Aesthetic complexity Kolmogorov complexity Image compression Art history Family resemblance Artistic careers |
| url | https://doi.org/10.1140/epjds/s13688-023-00397-3 |
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