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.
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