Top

Published in:

2020 | OriginalPaper | Chapter

Adapting and Enhancing Evolutionary Art for Casual Creation

Authors : Simon Colton, Jon McCormack, Sebastian Berns, Elena Petrovskaya, Michael Cook

Published in: Artificial Intelligence in Music, Sound, Art and Design

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Casual creators are creativity support tools designed for non-experts to have fun with while they create, rather than for serious creative production. We discuss here how we adapted and enhanced an evolutionary art approach for casual creation. Employing a fun-first methodology for the app design, we improved image production speed and the quality of randomly generated images. We further employed machine vision techniques for image categorisation and clustering, and designed a user interface for fast, fun image generation, adhering to numerous principles arising from the study of casual creators. We describe the implementation and experimentation performed during the first stage of development, and evaluate the app in terms of efficiency, image quality, feedback quality and the potential for users to have fun. We conclude with a description of how the app, which is destined for public release, will also be used as a research platform and as part of an art installation.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter A Deep Learning Neural Network for Classifying Good and Bad Photos

next chapter Comparing Fuzzy Rule Based Approaches for Music Genre Classification

Blair, A.: Adversarial evolution and deep learning – how does an artist play with our visual system? In: Ekárt, A., Liapis, A., Castro Pena, M.L. (eds.) EvoMUSART 2019. LNCS, vol. 11453, pp. 18–34. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-16667-0_2CrossRef

Blythe, M., Monk, A.: Funology 2: From Usability to Enjoyment. Springer, Heidelberg (2018). https://doi.org/10.1007/978-3-319-68213-6CrossRef

Colton, S., Cook, M., Raad, A.: Ludic considerations of tablet-based evo-art. In: Di Chio, C., et al. (eds.) EvoApplications 2011. LNCS, vol. 6625, pp. 223–233. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20520-0_23CrossRef

Colton, S.: Evolving a library of artistic scene descriptors. In: Machado, P., Romero, J., Carballal, A. (eds.) EvoMUSART 2012. LNCS, vol. 7247, pp. 35–47. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29142-5_4CrossRef

Colton, S., Pease, A., Cook, M., Chen, C.: The HR3 system for automated code generation in creative settings. In: Proceedings of the 10th ICCC (2019)

Compton, K.: Casual creators: AI supported creativity for casual users. Ph.D. thesis, University of California, Santa Cruz (2019)

Compton, K., Mateas, M.: Casual creators. In: Proceedings of the 6th International Conference on Computational Creativity (2015)

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Li, F.-F.: ImageNet: a large-scale hierarchical image database. In: Proceedings of IEEE CVPR (2009)

Elgammal, A., Liu, B., Elhoseiny, M., Mazzone, M.: CAN: Creative adversarial networks. In: Proceedings of the 8th ICCC (2017)

10.

Elsayed, G., et al.: Adversarial examples that fool both computer vision and time-limited humans. In: Proceedings of 32nd International Conference on Neural Information Processing Systems (2018)

11.

Gatys, L., Ecker, A., Bethge, M.: A neural algorithm of artistic style. In: Proceedings of IEEE Computer Vision and Pattern Recognition Conference (2016)

12.

Gaudl, S., et al.: Rapid game jams with fluidic games. Entertain. Comput. 27, 1–9 (2018)CrossRef

13.

Goodfellow, I., et al.: Generative adversarial networks. In: Proceedings of NIPS (2014)

14.

Han, S., Mao, H., Dally, W.: Deep compression: compressing deep neural networks with pruning, trained quantization and Huffman coding. In: Proceedings of ICLR (2015)

15.

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of IEEE Computer Vision and Pattern Recognition Conference (2016)

16.

Hull, M., Colton, S.: Towards a general framework for program generation in creative domains. In: Proceedings of 4th International Joint Workshop on computational Creativity (2007)

17.

Iandola, F., Han, S., Moskewicz, W., Ashraf, K., Dally, W., Keutzer, K.: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters. arXiv:1602.07360 (2016)

18.

Iyengar, S., Lepper, M.: When choice is demotivating: can one desire too much of a good thing? J. Pers. Soc. Psychol. 79, 995 (2000)CrossRef

19.

Liu, Y., Chen, X., Liu, C., Song, D.: Delving into transferable adversarial examples and black-box attacks. In: Proceedings of International Conference on Learning Representations (2016)

20.

McCormack, J.: Aesthetic evolution of L-systems revisited. In: Raidl, G., et al. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 477–488. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24653-4_49CrossRef

21.

Nelson, M., Gaudl, S., Colton, S., Deterding, S.: Curious users of casual creators. In: Proceedings of the 13th Conference on the Foundations of Digital Games (2018)

22.

Romero, J., Machado, P.: The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-72877-1CrossRef

23.

Sandler, M., Howard, A., Zhu, M., Zhmoginov, M., Chen, L.-C.: MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of IEEE CVPR (2018)

24.

Shneiderman, B.: Creativity support tools: accelerating discovery and innovation. Commun. ACM 50(12), 20–32 (2007)CrossRef

25.

Singh, D., Rajcic, N., Colton, S., McCormack, J.: Camera obscurer: generative art for design inspiration. In: Ekárt, A., Liapis, A., Castro Pena, M.L. (eds.) EvoMUSART 2019. LNCS, vol. 11453, pp. 51–68. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-16667-0_4CrossRef

26.

Szegedy, C., et al.: Intriguing properties of neural networks. In: Proceedings of ICLR (2013)

27.

Tanjil, F., Ross, B.J.: Deep learning concepts for evolutionary art. In: Ekárt, A., Liapis, A., Castro Pena, M.L. (eds.) EvoMUSART 2019. LNCS, vol. 11453, pp. 1–17. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-16667-0_1CrossRef

28.

Todd, S., Latham, W.: Evolutionary Art and Computers. Academic Press, Cambridge (1992)MATH

29.

White, T.: Perception engines (2018). https://medium.com/artists-and-machine-intelligence/perception-engines-8a46bc598d57

30.

Yannakakis, G., Liapis, A., Alexopoulos, C.: Mixed-initiative cocreativity. In: Proceedings of the 9th Conference on the Foundations of Digital Games (2014)

Title: Adapting and Enhancing Evolutionary Art for Casual Creation
Authors: Simon Colton
Jon McCormack
Sebastian Berns
Elena Petrovskaya
Michael Cook
Publisher: Springer International Publishing
Book: Artificial Intelligence in Music, Sound, Art and Design
Print ISBN: 978-3-030-43858-6

Electronic ISBN: 978-3-030-43859-3

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-43859-3_2

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner