Advertisement

PHOG-Derived Aesthetic Measures Applied to Color Photographs of Artworks, Natural Scenes and Objects

  • Christoph Redies
  • Seyed Ali Amirshahi
  • Michael Koch
  • Joachim Denzler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7583)

Abstract

Previous research in computational aesthetics has led to the identification of multiple image features that, in combination, can be related to the aesthetic quality of images, such as photographs. Moreover, it has been shown that aesthetic artworks possess specific higher-order statistical properties, such as a scale-invariant Fourier spectrum, that can be linked to coding mechanisms in the human visual system. In the present work, we derive novel measures based on a PHOG representation of images for image properties that have been studied in the context of the aesthetic assessment of images previously. We demonstrate that a large dataset of colored aesthetic paintings of Western provenance is characterized by a specific combination of the PHOG-derived aesthetic measures (high self-similarity, moderate complexity and low anisotropy). In this combination, the artworks differ significantly from seven other datasets of photographs that depict various types of natural and man-made scenes, patterns and objects. To the best of our knowledge, this is the first time that these features have been derived and evaluated on a large dataset of different image categories.

Keywords

Aesthetic art self-similarity complexity anisotropy Birkhoff-like measure Pyramid of Histograms of Orientation Gradients (PHOG) 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hoenig, F.: Defining computational aesthetics. EG Assoc., Goslar (2005)Google Scholar
  2. 2.
    Li, C., Chen, T.: Aesthetic visual quality assessment of paintings. IEEE J. Sel. Topics Signal Process. 3, 236–252 (2009)CrossRefGoogle Scholar
  3. 3.
    Datta, R., Joshi, D., Li, J., Wang, J.Z.: Studying Aesthetics in Photographic Images Using a Computational Approach. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part III. LNCS, vol. 3953, pp. 288–301. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Ke, Y., Tang, X., Jing, F.: The design of high-level features for photo quality assessment. In: Proceed. CVPR, pp. 419–426 (2006)Google Scholar
  5. 5.
    Xue, S.F., Lin, Q., Tretter, D., Lee, S., Pizlo, Z., Allebach, J.: Investigation of the role of aesthetics in differentiating between photographs taken by amateur and professional photographers. In: Proceed. SPIE, vol. 8302, p. 83020D (2012)Google Scholar
  6. 6.
    Zeki, S.: Art and the brain. J. Conscious Stud. 6-7, 76–96 (1999)Google Scholar
  7. 7.
    Rigau, J., Feixas, M., Sbert, M.: Informational aesthetics measures. IEEE Comput. Graph Appl. 28, 24–34 (2008)CrossRefGoogle Scholar
  8. 8.
    Taylor, R.P.: Order in Pollack’s chaos - computer analysis is helping to explain the appeal of Jackson Pollock’s paintings. Sci. Am. 287, 116–121 (2002)CrossRefGoogle Scholar
  9. 9.
    Redies, C.: A universal model of esthetic perception based on the sensory coding of natural stimuli. Spat Vis. 21, 97–117 (2007)CrossRefGoogle Scholar
  10. 10.
    Graham, D., Redies, C.: Statistical regularities in art: Relations with visual coding and perception. Vision Res. 50, 1503–1509 (2010)CrossRefGoogle Scholar
  11. 11.
    Graham, D.J., Field, D.J.: Statistical regularities of art images and natural scenes: spectra, sparseness and nonlinearities. Spat. Vis. 21, 149–164 (2007)CrossRefGoogle Scholar
  12. 12.
    Birkhoff, G.: Aesthetic Measure. Harvard University Press, Cambridge (1933)zbMATHGoogle Scholar
  13. 13.
    Redies, C., Hasenstein, J., Denzler, J.: Fractal-like image statistics in visual art: similarity to natural scenes. Spat. Vis. 21, 97–117 (2007)CrossRefGoogle Scholar
  14. 14.
    Arnheim, R.: Art and Visual Perception: A Psychology of the Creative Eye. University of California Press (2004)Google Scholar
  15. 15.
    Taylor, R.P., Spehar, B., Van Donkelaar, P., Hagerhall, C.: Perceptual and physiological responses to Jackson Pollock’s fractals. Front. Hum. Neurosci. 5, 60 (2011)CrossRefGoogle Scholar
  16. 16.
    Koch, M., Denzler, J., Redies, C.: 1/f 2 characteristics and isotropy in the Fourier power spectra of visual art, cartoons, comics, mangas, and different categories of photographs. PLoS one 5(8), e12268 (2010)Google Scholar
  17. 17.
    Redies, C., Hänisch, J., Blickhan, M., Denzler, J.: Artists portray human faces with the Fourier statistics of complex natural scenes. Network 18(3), 235–248 (2007)CrossRefGoogle Scholar
  18. 18.
    Amirshahi, S.A., Koch, M., Denzler, J., Redies, C.: PHOG analysis of self-similarity in esthetic images. In: Proceed SPIE (HVEI XVII), vol. 8291, p. 82911J (2012)Google Scholar
  19. 19.
    Bosch, A., Tisserman, A., Munoz, X.: Representing shape with a spatial pyramid kernel. In: Proceed. CIVR (2007)Google Scholar
  20. 20.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceed. CVPR, pp. 886–893 (2005)Google Scholar
  21. 21.
    Barla, A., Franceschi, E., Odone, F., Verri, A.: Image Kernels. In: Lee, S.-W., Verri, A. (eds.) SVM 2002. LNCS, vol. 2388, pp. 83–96. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  22. 22.
    Jacobsen, T., Hofel, L.: Aesthetic judgments of novel graphic patterns: analyses of individual judgments. Percept. Mot. Skills 95, 755–766 (2002)Google Scholar
  23. 23.
    Orians, G.: An ecological and evolutionary approach to landscape aesthetics. Allen and Unwin, London (1986)Google Scholar
  24. 24.
    Boselie, F., Leeuwenberg, E.: Birkhoff revisited: beauty as a function of effect and means. Am. J. Psychol. 98(1), 1–39 (1985)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Christoph Redies
    • 2
  • Seyed Ali Amirshahi
    • 1
    • 2
  • Michael Koch
    • 1
    • 2
  • Joachim Denzler
    • 1
  1. 1.Computer Vision GroupFriedrich Schiller University JenaGermany
  2. 2.Institute of Anatomy IFriedrich Schiller University, Jena University HospitalGermany

Personalised recommendations