A Complexity Approach for Identifying Aesthetic Composite Landscapes

  • Adrian Carballal
  • Rebeca Perez
  • Antonino Santos
  • Luz Castro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8601)


The present paper describes a series of features related to complexity which may allow to estimate the complexity of an image as a whole, of all the elements integrating it and of those which are its focus of attention. Using a neural network to create a classifier based on those features an accuracy over 85% in an aesthetic composition binary classification task is achieved. The obtained network seems to be useful for the purpose of assessing the Aesthetic Composition of landscapes. It could be used as part of a media device for facilitating the creation of images or videos with a more professional aesthetic composition.


Original Image Complexity Approach Saliency Detection Sobel Filter Interactive Evolutionary Computation 
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.
    Forsythe, A., Nadal, M., Sheehy, N., Cela-Conde, C.J., Sawey, M.: Predicting beauty: Fractal dimension and visual complexity in art. British Journal of Psychology 102(1), 49–70 (2011)CrossRefGoogle Scholar
  3. 3.
    Galanter, P.: What is generative art? complexity theory as a context for art theory. In: International Conference on Generative Art, Milan, Italy (2003)Google Scholar
  4. 4.
    Levin, G., Feinberg, J., Curtis, C.: Alphabet synthesis machine (2006),
  5. 5.
    Liu, L., Chen, R., Wolf, L., Cohen-Or, D.: Optimizing photo composition. Comput. Graph. Forum 29(2), 469–478 (2010)CrossRefGoogle Scholar
  6. 6.
    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
  7. 7.
    Machado, P., Cardoso, A.: All the truth about NEvAr. Applied Intelligence, Special Issue on Creative Systems 16(2), 101–119 (2002)zbMATHGoogle Scholar
  8. 8.
    Machado, P., Romero, J., Cardoso, A., Santos, A.: Partially interactive evolutionary artists. New Generation Computing – Special Issue on Interactive Evolutionary Computation 23(42), 143–155 (2005)Google Scholar
  9. 9.
    Machado, P., Cardoso, A.: Computing aesthetics. In: de Oliveira, F.M. (ed.) SBIA 1998. LNCS (LNAI), vol. 1515, pp. 219–228. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  10. 10.
    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
  11. 11.
    Rigau, J., Feixas, M., Sbert, M.: Informational dialogue with van gogh’s paintings. In: Eurographics Symposium on Computational Aesthetics in Graphics, Visualization and Imaging, pp. 115–122 (June 2008)Google Scholar
  12. 12.
    Romero, J., Machado, P., Carballal, A., Correia, J.: Computing aesthetics with image judgement systems. In: McCormack, J., do, M. (eds.) Computers and Creativity, pp. 295–322. Springer, Heidelberg (2012), CrossRefGoogle Scholar
  13. 13.
    Romero, J., Machado, P., Carballal, A., Osorio, O.: Aesthetic classification and sorting based on image compression. In: Chio, C.D., et al. (eds.) EvoApplications 2011, Part II. LNCS, vol. 6625, pp. 394–403. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  14. 14.
    Romero, J., Machado, P., Carballal, A., Santos, A.: Using complexity estimates in aesthetic image classification. Journal of Mathematics and the Arts 6(2-3), 125–136 (2012)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Romero, J., Machado, P., Santos, A., Cardoso, A.: On the development of critics in evolutionary computation artists. In: Raidl, G.R., et al. (eds.) EvoWorkshops 2003. LNCS, vol. 2611, pp. 559–569. Springer, Heidelberg (2003)Google Scholar
  16. 16.
    Ross, B.J., Ralph, W., Hai, Z.: Evolutionary image synthesis using a model of aesthetics. In: Yen, G.G., Lucas, S.M., Fogel, G., Kendall, G., Salomon, R., Zhang, B.T., Coello, C.A.C., Runarsson, T.P. (eds.) Proceedings of the 2006 IEEE Congress on Evolutionary Computation, July 16–21, pp. 1087–1094. IEEE Press, Vancouver (2006)CrossRefGoogle Scholar
  17. 17.
    Santella, A., Agrawala, M., DeCarlo, D., Salesin, D., Cohen, M.: Gaze-based interaction for semi-automatic photo cropping. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2006, pp. 771–780. ACM, New York (2006)Google Scholar
  18. 18.
    Suh, B., Ling, H., Bederson, B.B., Jacobs, D.W.: Automatic thumbnail cropping and its effectiveness. In: UIST, pp. 95–104. ACM (2003)Google Scholar
  19. 19.
    Wang, J., Cohen, M.F.: Simultaneous matting and compositing. In: Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007). IEEE Computer Society (2007)Google Scholar
  20. 20.
    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
  21. 21.
    Zhang, M., Zhang, L., Sun, Y., Feng, L., Ma, W.Y.: Auto cropping for digital photographs. In: ICME, pp. 438–441 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Adrian Carballal
    • 1
  • Rebeca Perez
    • 1
  • Antonino Santos
    • 1
  • Luz Castro
    • 1
  1. 1.Department of Information and Communication TechnologiesUniversity of A CoruñaA CoruñaSpain

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