Image catalogs containing several million reproductions of artworks still pose a costly or computationally intensive challenge if one tries to categorize them adequately, either in a manual or automatic way. Using crowdsourced annotations assigned by laypersons, this article proposes the application of a clustering algorithm to segment artworks into groups. It is shown that the resulting clusters allow for a consistent reclassification extending the traditional categories (history, genre, portrait, still life, landscape), and thus enable a finely-grained differentiation which can be used to search in and filter image inventories, among other things.



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