Aesthetic Classification and Sorting Based on Image Compression

  • Juan Romero
  • Penousal Machado
  • Adrian Carballal
  • Olga Osorio
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6625)


One of the problems in evolutionary art is the lack of robust fitness functions. This work explores the use of image compression estimates to predict the aesthetic merit of images. The metrics proposed estimate the complexity of an image by means of JPEG and Fractal compression. The success rate achieved is 72.43% in aesthetic classification tasks of a problem belonging to the state of the art. Finally, the behavior of the system is shown in an image sorting task based on aesthetic criteria.


Support Vector Machine Fractal Compression Image Compression Grayscale Image Aesthetic Quality 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Arnheim, R.: Art and Visual Perception, a psychology of the creative eye. Faber and Faber, London (1956)Google Scholar
  2. 2.
    Birkhoff, G.D.: Aesthetic Measure. Harvard University Press, Cambridge (1932)zbMATHGoogle Scholar
  3. 3.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), software available at
  4. 4.
    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. LNCS, vol. 3953, pp. 288–301. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  5. 5.
    Eysenck, H.J.: The empirical determination of an aesthetic formula. Psychological Review 48, 83–92 (1941)CrossRefGoogle Scholar
  6. 6.
    Eysenck, H.J.: The experimental study of the ’Good Gestalt’ - A new approach. Psychological Review 49, 344–363 (1942)CrossRefGoogle Scholar
  7. 7.
    Fisher, Y. (ed.): Fractal Image Compression: Theory and Application. Springer, London (1995)Google Scholar
  8. 8.
    Graves, M.: Design Judgment Test. The Psychological Corporation, New York (1948)Google Scholar
  9. 9.
    Greenfield, G., Machado, P.: Simulating artist and critic dynamics - an agent-based application of an evolutionary art system. In: Dourado, A., Rosa, A.C., Madani, K. (eds.) IJCCI, pp. 190–197. INSTICC Press (2009)Google Scholar
  10. 10.
    Ke, Y., Tang, X., Jing, F.: The Design of High-Level Features for Photo Quality Assessment. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 419–426 (2006)Google Scholar
  11. 11.
    Luo, Y., Tang, X.: Photo and video quality evaluation: Focusing on the subject. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 386–399. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  12. 12.
    Machado, P., Cardoso, A.: Computing aesthetics. In: de Oliveira, F.M. (ed.) SBIA 1998. LNCS (LNAI), vol. 1515, pp. 219–229. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  13. 13.
    Machado, P., Romero, J., Manaris, B.: Experiments in Computational Aesthetics. In: The Art of Artificicial Evolution. Springer, Heidelberg (2007)Google Scholar
  14. 14.
    Machado, P., Romero, J., Manaris, B.: Experiments in computational aesthetics: An iterative approach to stylistic change in evolutionary art. In: Romero, J., Machado, P. (eds.) The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music, pp. 381–415. Springer, Heidelberg (2007)Google Scholar
  15. 15.
    Machado, P., Romero, J., Santos, A., Cardoso, A., Manaris, B.: Adaptive critics for evolutionary artists. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 435–444. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  16. 16.
    Meier, N.C.: Art in human affairs. McGraw-Hill, New York (1942)Google Scholar
  17. 17.
    Moles, A.: Theorie de l’information et perception esthetique, Denoel (1958)Google Scholar
  18. 18.
    Tong, H., Li, M., Zhang, H., He, J., Zhang, C.: Classification of Digital Photos Taken by Photographers or Home Users. In: Aizawa, K., Nakamura, Y., Satoh, S. (eds.) PCM (1). LNCS, vol. 3332, pp. 198–205. Springer, Heidelberg (2004)Google Scholar
  19. 19.
    Witten, I.H., Frank, E.: Data mining: practical machine learning tools and techniques with java implementations. SIGMOD Rec. 31(1), 76–77 (2002)CrossRefGoogle Scholar
  20. 20.
    Wong, L., Low, K.: Saliency-enhanced image aesthetics class prediction. In: ICIP 2009, pp. 997–1000. IEEE, Los Alamitos (2009)CrossRefGoogle Scholar
  21. 21.
    Zell, A., Mamier, G., Vogt, M., Mache, N., Hübner, R., Döring, S., Herrmann, K.U., Soyez, T., Schmalzl, M., Sommer, T., et al.: SNNS: Stuttgart Neural Network Simulator User Manual, version 4.2. Tech. Rep. 3/92, University of Stuttgart, Stuttgart (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Juan Romero
    • 1
  • Penousal Machado
    • 2
  • Adrian Carballal
    • 1
  • Olga Osorio
    • 3
  1. 1.Faculty of Computer ScienceUniversity of A CoruñaCoruñaSpain
  2. 2.CISUC, Department of Informatics EngineeringUniversity of CoimbraCoimbraPortugal
  3. 3.Faculty of Communication SciencesUniversity of A CoruñaCoruñaSpain

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