Evolutionary Image Transition Using Random Walks

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10198)

Abstract

We present a study demonstrating how random walk algorithms can be used for evolutionary image transition. We design different mutation operators based on uniform and biased random walks and study how their combination with a baseline mutation operator can lead to interesting image transition processes in terms of visual effects and artistic features. Using feature-based analysis we investigate the evolutionary image transition behaviour with respect to different features and evaluate the images constructed during the image transition process.

Notes

Acknowledgement

This work has been supported through Australian Research Council (ARC) grant DP140103400.

References

  1. 1.
    Romero, J., Machado, P. (eds.): The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music. Natural Computing Series. Springer, Heidelberg (2008)Google Scholar
  2. 2.
    Antunes, R.F., Leymarie, F.F., Latham, W.H.: On writing and reading artistic computational ecosystems. Artif. Life 21(3), 320–331 (2015)CrossRefGoogle Scholar
  3. 3.
    Lambert, N., Latham, W.H., Leymarie, F.F.: The emergence and growth of evolutionary art: 1980–1993. In: International Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2013, Anaheim, CA, USA, July 21–25, 2013, Art Gallery, 367–375. ACM (2013)Google Scholar
  4. 4.
    McCormack, J., d’Inverno, M. (eds.): Computers and Creativity. Springer, Heidelberg (2012)Google Scholar
  5. 5.
    Vinhas, A., Assunção, F., Correia, J., Ekárt, A., Machado, P.: Fitness and novelty in evolutionary art. In: Johnson, C., Ciesielski, V., Correia, J., Machado, P. (eds.) EvoMUSART 2016. LNCS, vol. 9596, pp. 225–240. Springer, Cham (2016). doi:10.1007/978-3-319-31008-4_16 CrossRefGoogle Scholar
  6. 6.
    al-Rifaie, M.M., Bishop, J.M.: Swarmic paintings and colour attention. In: Machado, P., McDermott, J., Carballal, A. (eds.) EvoMUSART 2013. LNCS, vol. 7834, pp. 97–108. Springer, Heidelberg (2013). doi:10.1007/978-3-642-36955-1_9 CrossRefGoogle Scholar
  7. 7.
    Greenfield, G.: Avoidance drawings evolved using virtual drawing robots. In: Johnson, C., Carballal, A., Correia, J. (eds.) EvoMUSART 2015. LNCS, vol. 9027, pp. 78–88. Springer, Cham (2015). doi:10.1007/978-3-319-16498-4_8 Google Scholar
  8. 8.
    Todd, S., Latham, W.: Evolutionary Art and Computers. Academic Press Inc., Orlando (1994)MATHGoogle Scholar
  9. 9.
    Greenfield, G., Machado, P.: Ant- and ant-colony-inspired alife visual art. Artif. Life 21(3), 293–306 (2015)CrossRefGoogle Scholar
  10. 10.
    Machado, P., Correia, J.: Semantic aware methods for evolutionary art. In: Arnold, D.V., (ed.) Genetic and Evolutionary Computation Conference, GECCO 2014, Vancouver, BC, Canada, 12–16 July 2014, pp. 301–308. ACM (2014)Google Scholar
  11. 11.
    Sims, K.: Artificial evolution for computer graphics. In: Thomas, J.J., (ed.) Proceedings of the 18th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 1991, pp. 319–328. ACM (1991)Google Scholar
  12. 12.
    Hart, D.A.: Toward greater artistic control for interactive evolution of images and animation. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 527–536. Springer, Heidelberg (2007). doi:10.1007/978-3-540-71805-5_58 Google Scholar
  13. 13.
    Trist, K., Ciesielski, V., Barile, P.: An artist’s experience in using an evolutionary algorithm to produce an animated artwork. IJART 4(2), 155–167 (2011)CrossRefGoogle Scholar
  14. 14.
    Graf, J., Banzhaf, W.: Interactive evolution of images. In: Evolutionary Programming, pp. 53–65 (1995)Google Scholar
  15. 15.
    Karungaru, S., Fukumi, M., Akamatsu, N., Takuya, A.: Automatic human faces morphing using genetic algorithms based control points selection. Int. J. Innovative Comput. Inf. Control 3(2), 1–6 (2007)Google Scholar
  16. 16.
    Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2414–2423 (2016)Google Scholar
  17. 17.
    Neumann, A., Alexander, B., Neumann, F.: The evolutionary process of image transition in conjunction with box and strip mutation. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds.) ICONIP 2016. LNCS, vol. 9949, pp. 261–268. Springer, Cham (2016). doi:10.1007/978-3-319-46675-0_29 CrossRefGoogle Scholar
  18. 18.
    Jansen, T., Sudholt, D.: Analysis of an asymmetric mutation operator. Evol. Comput. 18(1), 1–26 (2010)CrossRefGoogle Scholar
  19. 19.
    Witt, C.: Tight bounds on the optimization time of a randomized search heuristic on linear functions. Comb. Probab. Comput. 22(2), 294–318 (2013)MathSciNetCrossRefMATHGoogle Scholar
  20. 20.
    Sudholt, D.: A new method for lower bounds on the running time of evolutionary algorithms. IEEE Trans. Evol. Comput. 17(3), 418–435 (2013)CrossRefGoogle Scholar
  21. 21.
    Lovász, L.: Random walks on graphs: A survey. In: Miklós, D., Sós, V.T., Szőnyi, T. (eds.) Combinatorics, Paul Erdős is Eighty, vol. 2, pp. 353–398. János Bolyai Mathematical Society, Budapest (1996)Google Scholar
  22. 22.
    Dembo, A., Peres, Y., Rosen, J., Zeitouni, O.: Cover times for brownian motion and random walks in two dimensions. Ann. Math. 160(2), 433–464 (2004)MathSciNetCrossRefMATHGoogle Scholar
  23. 23.
    Mersmann, O., Preuss, M., Trautmann, H.: Benchmarking evolutionary algorithms: Towards exploratory landscape analysis. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN 2010. LNCS, vol. 6238, pp. 73–82. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15844-5_8 Google Scholar
  24. 24.
    Mersmann, O., Bischl, B., Trautmann, H., Wagner, M., Bossek, J., Neumann, F.: A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem. Ann. Math. Artif. Intell. 69(2), 151–182 (2013)MathSciNetCrossRefMATHGoogle Scholar
  25. 25.
    Nallaperuma, S., Wagner, M., Neumann, F., Bischl, B., Mersmann, O., Trautmann, H.: A feature-based comparison of local search and the christofides algorithm for the travelling salesperson problem. In: Neumann, F., Jong, K.A.D., (eds.) Foundations of Genetic Algorithms XII, FOGA 2013, Adelaide, SA, Australia, 16–20 January 2013, pp. 147–160. ACM (2013)Google Scholar
  26. 26.
    Nallaperuma, S., Wagner, M., Neumann, F.: Analyzing the effects of instance features and algorithm parameters for max-min ant system and the traveling salesperson problem. Front. Robot. AI 2, 1–16 (2015)CrossRefGoogle Scholar
  27. 27.
    Poursoltan, S., Neumann, F.: A feature-based prediction model of algorithm selection for constrained continuous optimisation. CoRR abs/1602.02862 Conference version appeared in CEC 2016(2016)Google Scholar
  28. 28.
    Neumann, F., Wegener, I.: Randomized local search, evolutionary algorithms, and the minimum spanning tree problem. Theor. Comput. Sci. 378(1), 32–40 (2007)MathSciNetCrossRefMATHGoogle Scholar
  29. 29.
    Grady, L.: Random walks for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 28(11), 1768–1783 (2006)CrossRefGoogle Scholar
  30. 30.
    Mitzenmacher, M., Upfal, E.: Probability and Computing: Randomized Algorithms and Probabilistic Analysis. Cambridge University Press, New York (2005)CrossRefMATHGoogle Scholar
  31. 31.
    Jolion, J.M.: Images and benford’s law. J. Math. Imaging Vis. 14(1), 73–81 (2001)MathSciNetCrossRefMATHGoogle Scholar
  32. 32.
    Matkovic, K., Neumann, L., Neumann, A., Psik, T., Purgathofer, W.: Global contrast factor-a new approach to image contrast. Comput. Aesthetics 2005, 159–168 (2005)Google Scholar
  33. 33.
    Hasler, D., Suesstrunk, S.E.: Measuring colorfulness in natural images. In: Electronic Imaging 2003, International Society for Optics and Photonics, pp. 87–95 (2003)Google Scholar
  34. 34.
    den Heijer, E., Eiben, A.E.: Investigating aesthetic measures for unsupervised evolutionary art. Swarm Evol. Comput. 16, 52–68 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Aneta Neumann
    • 1
  • Bradley Alexander
    • 1
  • Frank Neumann
    • 1
  1. 1.Optimisation and Logistics, School of Computer ScienceThe University of AdelaideAdelaideAustralia

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