Supervised Genetic Search for Parameter Selection in Painterly Rendering

  • John P. Collomosse
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3907)


This paper investigates the feasibility of evolutionary search techniques as a mechanism for interactively exploring the design space of 2D painterly renderings. Although a growing body of painterly rendering literature exists, the large number of low-level configurable parameters that feature in contemporary algorithms can be counter-intuitive for non-expert users to set. In this paper we first describe a multi-resolution painting algorithm capable of transforming photographs into paintings at interactive speeds. We then present a supervised evolutionary search process in which the user scores paintings on their aesthetics to guide the specification of their desired painterly rendering. Using our system, non-expert users are able to produce their desired aesthetic in approximately 20 mouse clicks — around half an order of magnitude faster than manual specification of individual rendering parameters by trial and error.


Evolutionary Search Brush Stroke Genetic Algorithm Search Artistic Style Interactive Genetic Algorithm 
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.
    Curtis, C., Anderson, S., Seims, J., Fleischer, K., Salesin, D.H.: Computergenerated watercolor. In: Proc. ACM SIGGRAPH, pp. 421–430 (1997)Google Scholar
  2. 2.
    Litwinowicz, P.: Processing images and video for an impressionist effect. In: Proc. ACM SIGGRAPH, Los Angeles, USA, pp. 407–414 (1997)Google Scholar
  3. 3.
    Hertzmann, A.: Painterly rendering with curved brush strokes of multiple sizes. In: Proc. ACM SIGGRAPH, pp. 453–460 (1998)Google Scholar
  4. 4.
    Shiraishi, M., Yamaguchi, Y.: An algorithm for automatic painterly rendering based on local image approximation. In: Proc. ACM NPAR Sympos., pp. 53–58 (2000)Google Scholar
  5. 5.
    Gooch, B., Coombe, G., Shirley, P.: Artistic vision: Painterly rendering using computer vision techniques. In: Proc. ACM NPAR Sympos., pp. 83–90 (2002)Google Scholar
  6. 6.
    Hays, J., Essa, I.: Image and video based painterly animation. In: Proc. ACM NPAR Sympos., pp. 113–120 (2004)Google Scholar
  7. 7.
    Collomosse, J.P., Hall, P.M.: Genetic paint: A search for salient paintings. In: Rothlauf, F., Branke, J., Cagnoni, S., Corne, D.W., Drechsler, R., Jin, Y., Machado, P., Marchiori, E., Romero, J., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2005. LNCS, vol. 3449, pp. 437–447. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    Sims, K.: Artificial evolution for computer graphics. In: Proc. ACM SIGGRAPH, vol. 25, pp. 319–328 (1991)Google Scholar
  9. 9.
    Ebner, M., Reinhardt, M., Albert, J.: Evolution of vertex and pixel shaders. In: Keijzer, M., Tettamanzi, A.G.B., Collet, P., van Hemert, J., Tomassini, M. (eds.) EuroGP 2005. LNCS, vol. 3447, pp. 261–270. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Draves, S.: The electric sheep screen-saver: A case study in aesthetic evolution. In: Rothlauf, F., Branke, J., Cagnoni, S., Corne, D.W., Drechsler, R., Jin, Y., Machado, P., Marchiori, E., Romero, J., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2005. LNCS, vol. 3449, pp. 458–467. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Russell, J.A.: Reading emotion from and into faces: Resurrecting a dimensionalcontextual perspective. In: Russel, J.A., Fernández-Dols, J.M. (eds.) The Psychology of Facial Expression, pp. 295–320. Cambridge University Press, Cambridge (1997)CrossRefGoogle Scholar
  12. 12.
    Shugrina, M., Betke, M., Collomosse, J.P.: Empathic painting: Interactive stylization using observed emotional state. In: Proc. ACM NPAR Sympos. (2006)Google Scholar
  13. 13.
    Haeberli, P.: Paint by numbers: abstract image representations. In: Proc. ACM SIGGRAPH, vol. 4, pp. 207–214 (1990)Google Scholar
  14. 14.
    Hertzmann, A.: Paint by relaxation. In: Proc. Computer Graphics Intl. (CGI), pp. 47–54 (2001)Google Scholar
  15. 15.
    Treavett, S., Chen, M.: Statistical techniques for the automated synthesis of nonphotorealistic images. In: Proc. 15th Eurographics UK Conference, pp. 201–210 (1997)Google Scholar
  16. 16.
    DeCarlo, D., Santella, A.: Abstracted painterly renderings using eye-tracking data. In: Proc. ACM SIGGRAPH, pp. 769–776 (2002)Google Scholar
  17. 17.
    Santella, A., DeCarlo, D.: Visual interest and NPR: an evaluation and manifesto. In: Proc. ACM NPAR Sympos., pp. 71–78 (2004)Google Scholar
  18. 18.
    Christoudias, C., Georgescu, B., Meer, P.: Synergism in low level vision. In: 16th Intl. Conf. on Pattern Recognition, vol. 4, pp. 150–155 (2002)Google Scholar
  19. 19.
    Kolliopoulos, A.: Image segmentation for stylized non-photorealistic rendering and animation. Master’s thesis, Univ. Toronto (2005)Google Scholar
  20. 20.
    Wright, B., Rainwater, L.: The meaning of colour. Journal of General Psychology 67 (1962)Google Scholar
  21. 21.
    Mahnke, F.: Color, Environment, and Human Response. Van Nostrand Reinhold (1996)Google Scholar
  22. 22.
    de Jong, K.: Learning with genetic algorithms. Machine Learning 3, 121–138 (1988)CrossRefGoogle Scholar
  23. 23.
    Holland, J.: Adaptation in Natural and Artificial Systems, 1st edn. Univ. Michigan Press (1975) ISBN: 0-472-08460-7Google Scholar
  24. 24.
    Hertzmann, A., Perlin, K.: Painterly rendering for video and interaction. In: Proc. ACM NPAR Sympos., pp. 7–12 (2000)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • John P. Collomosse
    • 1
  1. 1.Department of Computer ScienceUniversity of BathBathUK

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