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  Categorizing art: Comparing humans and computers

Wallraven, C., Fleming, R., Cunningham, D., Rigau, J., Feixas, M., & Sbert, M. (2009). Categorizing art: Comparing humans and computers. Computers and Graphics, 33(4), 484-495. doi:10.1016/j.cag.2009.04.003.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C357-D Version Permalink: http://hdl.handle.net/21.11116/0000-0002-BA56-C
Genre: Journal Article

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Wallraven, C1, 2, Author              
Fleming, R1, 2, 3, Author              
Cunningham, DW, Author              
Rigau , J, Author
Feixas, M, Author
Sbert, M, Author
Affiliations:
1Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497797              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              
3Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497805              

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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.

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 Dates: 2009-08
 Publication Status: Published in print
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 Rev. Method: -
 Identifiers: DOI: 10.1016/j.cag.2009.04.003
BibTex Citekey: 5741
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Title: Computers and Graphics
Source Genre: Journal
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Publ. Info: New York : Pergamon
Pages: - Volume / Issue: 33 (4) Sequence Number: - Start / End Page: 484 - 495 Identifier: ISSN: 0097-8493
CoNE: https://pure.mpg.de/cone/journals/resource/954925466253