We’ve all seen art made from data, but what about data from art?
In a feature paper in Entropy, Bhargav Srinivasa Desikan (École Polytechnique Fédérale de Lausanne), Hajime Shimao (McGill University, former SFI Postdoctoral Fellow), and SFI Complexity Postdoctoral Fellow Helena Miton released a novel dataset for indexing, searching, retrieving, organizing, and analyzing 68,094 works of art by more than 1,600 historically significant artists.
Using state-of-the-art machine learning, the authors were able to extract both style representations and color distributions, which can be used to query stylistic periods for an artist or a movement (e.g., Picasso’s “blue” phase).
Their dataset, WikiArtVectors, aims to make computational data approaches available to art historians and cultural analysts, to help discover and understand patterns of cultural evolution.
Bhargav Srinivasa Desikan et al, WikiArtVectors: Style and Color Representations of Artworks for Cultural Analysis via Information Theoretic Measures, Entropy (2022). DOI: 10.3390/e24091175
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A massive new dataset for understanding art (2022, November 4)
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