Perceptually-Inspired Artistic Genre Identification System in Digitized Painting Collections

  • Razvan George Condorovici
  • Corneliu Florea
  • Ruxandra Vrânceanu
  • Constantin Vertan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7944)


This paper presents an automatic system for the recognition of artistic genre in digital representations of paintings. This solution comes as part of the recent extensive effort of developing image processing solutions that facilitate a better understanding of art. As art addresses human perception, the current extracted features are perceptually inspired. While 3D Color Histogram and Gabor Filter Energy have been used for art description, frameworks extracted using anchoring theory are novel in this field. The paper investigates the possible use of 7 classifiers and the resulting performance, as evaluated on a database containing more than 3400 paintings from 6 different genres, outperforms the reported state of the art.


Paintings Image Classification Artistic Genre Anchoring Theory 3D Color Histogram Gabor Filters 


  1. 1.
    Cornelis, B., Dooms, A., Cornelis, J., Leen, F., Schelkens, P.: Digital painting analysis, at the cross section of engineering, mathematics and culture. In: Proc. of EUSIPCO, pp. 1254–1259 (2011)Google Scholar
  2. 2.
    Martinez, K., Cupitt, J., Saunders, D., Pillay, R.: Ten years of art imaging research. Proceedings of the IEEE 90(1), 28–41 (2002)CrossRefGoogle Scholar
  3. 3.
    Stork, D.G.: Computer vision and computer graphics analysis of paintings and drawings: An introduction to the literature. In: Jiang, X., Petkov, N. (eds.) CAIP 2009. LNCS, vol. 5702, pp. 9–24. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  4. 4.
    Gunsel, B., Sariel, S., Icoglu, O.: Content-based access to art paintings. In: Proc. of ICIP, pp. 558–561 (2005)Google Scholar
  5. 5.
    Zujovic, J., Gandy, L., Friedman, S., Pardo, B., Pappas, T.: Classifying paintings by artistic genre: An analysis of features & classifiers. In: Proc. of IEEE MMSP, pp. 1–5 (2009)Google Scholar
  6. 6.
    Li, J., Wang, J.: Studying digital imagery of ancient paintings by mixtures of stochastic models. IEEE Trans. on Image Proccessing 13(3), 340–353 (2004)CrossRefGoogle Scholar
  7. 7.
    Widjaja, I., Leow, W.K., Wu, F.: Identifying painters from color profiles of skin patches in painting images. In: Proc. of ICIP, pp. 845–848 (2003)Google Scholar
  8. 8.
    Shamir, L., Macura, T., Orlov, N., Eckley, D.M., Goldberg, I.G.: Impressionism, expressionism, surrealism: Automated recognition of painters and schools of art. ACM Transactions on Applied Perception 7(2), 1–17 (2010)CrossRefGoogle Scholar
  9. 9.
    Zeki, S.: Inner Vision. Oxford University Press (1999)Google Scholar
  10. 10.
    Ramachandran, V., Herstein, W.: The science of art: A neurological theory of aesthetic experience. Journal of Consciousness Studies 6, 15–51 (1999)Google Scholar
  11. 11.
    Gilchrist, A., Kossyfidis, C., Bonato, F., Agostini, T., Cataliotti, J., Li, X., Spehar, B., Annan, V., Economou, E.: An anchoring theory of lightness perception. Psychological Review 106(4), 795–834 (1999)CrossRefGoogle Scholar
  12. 12.
    Krawczyk, G., Myszkowski, K., Seidel, H.P.: Lightness perception in tone reproduction for high dynamic range images. In: Proc. of EUROGRAPHICS, Computer Graphics Forum., vol. 24 (2005)Google Scholar
  13. 13.
    Rappaport, A., Rapaport, A.: Color preferences, color harmony, and the quantitative use of colors. Empirical Studies of the Arts 2(2), 95–111 (1984)CrossRefGoogle Scholar
  14. 14.
    Novak, C.L., Shafer, S.: Anatomy of a color histogram. In: Proc. of CVPR, pp. 599–605 (1992)Google Scholar
  15. 15.
    Moshtagh, N.: Minimum volume enclosing ellipsoid. Convex Optimization (2005)Google Scholar
  16. 16.
    Melcher, D., Cavanagh, P.: Pictorial cues in art and in visual perception. In: Art and the Senses, pp. 359–394. Oxford University Press (2011)Google Scholar
  17. 17.
    Daugman, J.: Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. Journal of the Optical Society of America A 2(7), 1160–1169 (1985)CrossRefGoogle Scholar
  18. 18.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. ACM SIGKDD Explorations Newsletter 11(1), 10–18 (2009)CrossRefGoogle Scholar
  19. 19.
    Redies, C., Amirshahi, S.A., Koch, M., Denzler, J.: PHOG-derived aesthetic measures applied to color photographs of artworks, natural scenes and objects. In: Fusiello, A., Murino, V., Cucchiara, R. (eds.) ECCV 2012 Ws/Demos, Part I. LNCS, vol. 7583, pp. 522–531. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  20. 20.
    Wallraven, C., Fleming, R.W., Cunningham, D.W., Rigau, J., Feixas, M., Sbert, M.: Categorizing art: Comparing humans and computers. Computers & Graphics 33(4), 484–495 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Razvan George Condorovici
    • 1
  • Corneliu Florea
    • 1
  • Ruxandra Vrânceanu
    • 1
  • Constantin Vertan
    • 1
  1. 1.The Image Processing and Analysis LaboratoryUniversity “Politehnica” of BucharestBucharestRomania

Personalised recommendations