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.
Schneider, Stefanie and Hubertus Kohle. "The Computer as Filter Machine: A Clustering Approach to Categorize Artworks Based on a Social Tagging Network." Artl@s Bulletin 6, no. 3 (2017): Article 6.