Modelling the Quality of Visual Creations in Iconoscope

  • Antonios LiapisEmail author
  • Daniele Gravina
  • Emil Kastbjerg
  • Georgios N. Yannakakis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11899)


This paper presents the current state of the online game Iconoscope and analyzes the data collected from almost 45 months of continuous operation. Iconoscope is a freeform creation game which aims to foster the creativity of its users through diagrammatic lateral thinking, as users are required to depict abstract concepts as icons which may be misinterpreted by other users as different abstract concepts. From users’ responses collected from an online gallery of all icons drawn with Iconoscope, we collect a corpus of over 500 icons which contain annotations of visual appeal. Several machine learning algorithms are tested for their ability to predict the appeal of an icon from its visual appearance and other properties. Findings show the impact of the representation on the model’s accuracy and highlight how such a predictive model of quality can be applied to evaluate new icons (human-authored or generated).


Online game Human creativity Crowdsourcing Deep learning Mixed-initiative design Computational creators 


  1. 1.
    Ballard, D.H., Hinton, G.E., Sejnowski, T.J.: Parallel visual computation. Nature 306(5938), 21 (1983)CrossRefGoogle Scholar
  2. 2.
    Cachia, R., et al.: Creativity in schools in Europe: a survey of teachers (2009). Accessed Nov 2016
  3. 3.
    Clevert, D.A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (ELUs). arXiv preprint arXiv:1511.07289 (2015)
  4. 4.
    De Bono, E.: Lateral Thinking: Creativity Step by Step. Harper Collins (2010)Google Scholar
  5. 5.
    Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT press (2016)Google Scholar
  6. 6.
    Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)
  7. 7.
    Jeffrey, B., Craft, A.: Teaching creatively and teaching for creativity: distinctions and relationships. Educ. Stud. 30(1), 77–87 (2004)CrossRefGoogle Scholar
  8. 8.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (2012)Google Scholar
  9. 9.
    LeCun, Y., Bengio, Y., et al.: Convolutional networks for images, speech, and time series. Handb. Brain Theory Neural Netw. 3361(10), 1995 (1995)Google Scholar
  10. 10.
    Correia, J., Ciesielski, V., Liapis, A. (eds.): EvoMUSART 2017. LNCS, vol. 10198. Springer, Cham (2017). Scholar
  11. 11.
    Liapis, A., Hoover, A.K., Yannakakis, G.N., Alexopoulos, C., Dimaraki, E.V.: Motivating visual interpretations in iconoscope: designing a game for fostering creativity. In: Proceedings of the Conference on the Foundations of Digital Games (2015)Google Scholar
  12. 12.
    Liapis, A., Yannakakis, G.N., Alexopoulos, C., Lopes, P.: Can computers foster human users’ creativity? theory and praxis of mixed-initiative co-creativity. Digit. Cult. Educ. (DCE) 8(2), 136–152 (2016) Google Scholar
  13. 13.
    Makantasis, K., Doulamis, A., Doulamis, N., Psychas, K.: Deep learning based human behavior recognition in industrial workflows. In: Proceedings of International Conference on Image Processing, pp. 1609–1613. IEEE (2016)Google Scholar
  14. 14.
    Park, E., Han, X., Berg, T.L., Berg, A.C.: Combining multiple sources of knowledge in deep CNNS for action recognition. In: Proceedings of the Winter Conference on Applications of Computer Vision (WACV), pp. 1–8. IEEE (2016)Google Scholar
  15. 15.
    Plato, C.D.: The Collected Dialogues. Princeton University Press (1961)Google Scholar
  16. 16.
    Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Procedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)Google Scholar
  17. 17.
    Sawyer, K.: Educating for innovation. Thinking Skills Creativity 1, 41–48 (2006)CrossRefGoogle Scholar
  18. 18.
    Scaltsas, T., Alexopoulos, C.: Creating creativity through emotive thinking. In: Proceedings of the World Congress of Philosophy (2013)Google Scholar
  19. 19.
    Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: Advances in Neural Information Processing Systems (2014)Google Scholar
  20. 20.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  21. 21.
    Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)MathSciNetzbMATHGoogle Scholar
  22. 22.
    Srivastava, N., Salakhutdinov, R.R.: Multimodal learning with deep boltzmann machines. In: Advances in Neural Information Processing Systems (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Antonios Liapis
    • 1
    Email author
  • Daniele Gravina
    • 1
  • Emil Kastbjerg
    • 1
  • Georgios N. Yannakakis
    • 1
  1. 1.Institute of Digital GamesUniversity of MaltaMsidaMalta

Personalised recommendations