Feature Construction Using Genetic Programming for Classification of Images by Aesthetic Value

  • Andrew Bishop
  • Vic Ciesielski
  • Karen Trist
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8601)


Classification or rating of images according to their aesthetic quality has applications in areas such as image search, compression and photography. It requires the construction of features that are predictive of the aesthetic quality of an image. Constructing features manually for aesthetics prediction is challenging. We propose an approach to improve on manually designed features by constructing them using genetic programming and image processing operations implemented using OpenCV. We show that this approach can produce features that perform well. Classification accuracies of up to 81% on photographs and 92% on computationally generated images have been achieved. Both of these results significantly improve on existing manually designed features.


Genetic Programming Feature Construction Image Aesthetics 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ciesielski, V., Barile, P., Trist, K.: Finding image features associated with high aesthetic value by machine learning. In: Machado, P., McDermott, J., Carballal, A. (eds.) EvoMUSART 2013. LNCS, vol. 7834, pp. 47–58. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  2. 2.
    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
  3. 3.
    Ke, Y., Tang, X., Jing, F.: The design of high-level features for photo quality assessment. In: Fitzgibbon, A., Taylor, C.J., LeCun, Y. (eds.) CVPR (1), pp. 419–426. IEEE Computer Society (2006)Google Scholar
  4. 4.
    Krawiec, K.: Genetic programming-based construction of features for machine learning and knowledge discovery tasks. Genetic Programming and Evolvable Machines 3(4), 329–343 (2002)CrossRefzbMATHGoogle Scholar
  5. 5.
    Krawiec, K., Bhanu, B.: Coevolution and linear genetic programming for visual learning. In: Cantú-Paz, E., et al. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 332–343. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  6. 6.
    Roberts, M.E., Claridge, E.: Cooperative coevolution of image feature construction and object detection. In: Yao, X., et al. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 902–911. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Zhang, M., Ciesielski, V.: Genetic programming for multiple class object detection. In: Foo, N.Y. (ed.) AI 1999. LNCS, vol. 1747, pp. 180–192. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  8. 8.
    Zhang, M., Wong, P.: Genetic programming for medical classification: a program simplification approach. Genetic Programming and Evolvable Machines 9(3), 229–255 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Andrew Bishop
    • 1
  • Vic Ciesielski
    • 1
  • Karen Trist
    • 2
  1. 1.School of Computer Science and Information TechnologyRMIT UniversityMelbourneAustralia
  2. 2.School of Media and CommunicationRMIT UniversityMelbourneAustralia

Personalised recommendations