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Abstract

The categorization of art (paintings, literature) into distinct styles such as Expressionism, or Surrealism has had a profound influence on how art is presented, marketed, analyzed, and historicized. Here, we present results from human and computational experiments with the goal of determining to which degree such categories can be explained by simple, low-level appearance information in the image. Following experimental methods from perceptual psychology on category formation, naive, non-expert participants were first asked to sort printouts of artworks from different art periods into categories. Converting these data into similarity data and running a multi-dimensional scaling (MDS) analysis, we found distinct categories which corresponded sometimes surprisingly well to canonical art periods. The result was cross-validated on two complementary sets of artworks for two different groups of participants showing the stability of art interpretation. The second focus of this paper was on determining how far computational algorithms would be able to capture human performance or would be able in general to separate different art categories. Using several state-of-the-art algorithms from computer vision, we found that whereas low-level appearance information can give some clues about category membership, human grouping strategies included also much higher-level concepts.