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A quantitative approach to painting styles

Author

Listed:
  • Vieira, Vilson
  • Fabbri, Renato
  • Sbrissa, David
  • da Fontoura Costa, Luciano
  • Travieso, Gonzalo

Abstract

This research extends a method previously applied to music and philosophy (Vilson Vieira et al., 2012), representing the evolution of art as a time-series where relations like dialectics are measured quantitatively. For that, a corpus of paintings of 12 well-known artists from baroque and modern art is analyzed. A set of 99 features is extracted and the features which most contributed to the classification of painters are selected. The projection space obtained provides the basis to the analysis of measurements. These quantitative measures underlie revealing observations about the evolution of painting styles, specially when compared with other humanity fields already analyzed: while music evolved along a master–apprentice tradition (high dialectics) and philosophy by opposition, painting presents another pattern: constant increasing skewness, low opposition between members of the same movement and opposition peaks in the transition between movements. Differences between baroque and modern movements are also observed in the projected “painting space”: while baroque paintings are presented as an overlapped cluster, the modern paintings present minor overlapping and are disposed more widely in the projection than the baroque counterparts. This finding suggests that baroque painters shared aesthetics while modern painters tend to “break rules” and develop their own style.

Suggested Citation

  • Vieira, Vilson & Fabbri, Renato & Sbrissa, David & da Fontoura Costa, Luciano & Travieso, Gonzalo, 2015. "A quantitative approach to painting styles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 110-129.
  • Handle: RePEc:eee:phsmap:v:417:y:2015:i:c:p:110-129
    DOI: 10.1016/j.physa.2014.09.038
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    Cited by:

    1. Lahmiri, Salim, 2016. "Image characterization by fractal descriptors in variational mode decomposition domain: Application to brain magnetic resonance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 235-243.

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