An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
Distinguishing paintings from photographs
Computer Vision and Image Understanding
The Structure of Paintings
Psychophysics for perception of (in)determinate art
Proceedings of the 4th symposium on Applied perception in graphics and visualization
Proceedings of the 15th international conference on Multimedia
Informational Aesthetics Measures
IEEE Computer Graphics and Applications
Ontology-based annotation of paintings using transductive inference framework
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
Studying aesthetics in photographic images using a computational approach
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
In the eye of the beholder - perception of indeterminate art
Computational Aesthetics'07 Proceedings of the Third Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging
Informational dialogue with van Gogh's paintings
Computational Aesthetics'08 Proceedings of the Fourth Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging
NPAR '10 Proceedings of the 8th International Symposium on Non-Photorealistic Animation and Rendering
Customizing painterly rendering styles using stroke processes
Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Non-Photorealistic Animation and Rendering
Toward Auvers period: evolution of van Gogh's style
Computational Aesthetics'10 Proceedings of the Sixth international conference on Computational Aesthetics in Graphics, Visualization and Imaging
Journal of Visual Communication and Image Representation
Abstract painting with interactive control of perceptual entropy
ACM Transactions on Applied Perception (TAP)
How self-similar are artworks at different levels of spatial resolution?
Proceedings of the Symposium on Computational Aesthetics
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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.