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Conference Paper

Perceptual and Computational Categories in Art

MPS-Authors
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Wallraven,  C
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Cunningham,  DW
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Fleming,  R
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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CAE-2008-Wallraven.pdf
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Citation

Wallraven, C., Cunningham, D., & Fleming, R. (2008). Perceptual and Computational Categories in Art. In D. Cunningham, V. Interrante, P. Brown, & J. McCormack (Eds.), Computational Aesthetics 2008: Eurographics Workshop on Computational Aesthetics (CAe 2008) (pp. 131-138). Aire-la-Ville, Switzerland: Eurographics Association.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-C907-B
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 several perceptual experiments with the goal of determining whether such categories also have a perceptual foundation. Following experimental methods from perceptual psychology on category formation, naive, non-expert participants were 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 perceptual categories which did in some cases correspond to canonical art periods. Initial results from a comparison with several computational algorithms for image analysis and scene categorization are also reported.