Categorizing art: Comparing humans and computers

Christian Wallraven, Roland Fleming, Douglas Cunningham, Jaume Rigau, Miquel Feixas, Mateu Sbert

Research output: Contribution to journalArticlepeer-review

43 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)484-495
Number of pages12
JournalComputers and Graphics (Pergamon)
Volume33
Issue number4
DOIs
Publication statusPublished - 2009 Aug
Externally publishedYes

Keywords

  • Computational aesthetics
  • Computer vision
  • Human studies
  • Multi-dimensional scaling

ASJC Scopus subject areas

  • Engineering(all)
  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design

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