SBArt4 as Automatic Art and Live Performance Tool

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Tatsuo Unemi: SBArt4 as Automatic Art and Live Performance Tool. In: Generative Art 2011, 436-447.



SBArt4 is originally a breeding tool to produce abstract images and animations [1]. This paper describes a newly implemented functionality of automated evolution using some types of computational aesthetic measures as fitness criteria. Measures include information complexity, global contrast factor, histogram of color distribution, and so on. In addition to these measures for a still image, a method to evaluate the motion was introduced. They are useful to discard boring pieces but do not always fit with human user’s preference.

I introduced graphical user interface to allow the user to adjust the balance among different measures, and provided methods to let individuals migrate between a field for breeding and a population for evolution. It realized a wide range of intermediate production style between pure breeding and fully automated evolution.

Utilizing parallel processing, efficient algorithms, and synchronized sound effects, a new generative style of both live performance and fully automatic art became possible to be realized.

Extended Abstract


Used References

[1] T. Unemi, SBART 2.4: an IEC Tool for Creating 2D Images, Movies, and Collage, Leonardo, Vol. 35, No. 2, pp. 171, 189-191, MIT Press, 2002.

[2] H. Takagi, Interactive Evolutionary Computation: Fusion of the Capabilities of EC Optimization and Human Evaluation, Proceedings of the IEEE, Vol. 89, No. 9, pp. 1275-1296, 2001.

[3] T. Unemi, SBArt4 - Breeding Abstract Animations in Real time, Proceedings of the Conference on Evolutionary Computation 2010, pp. 4004-4009, Barcelona, Spain, 2010.

[4] L. Neumann, M. Sbert, B. Gooch and W. Purgathofer (Eds.):. Computational Aesthetics 2005: Eurographics Workshop on Computational Aesthetics in Graphics, Visualization and Imaging, May 2005.

[5] K. Sims: Artificial Evolution for Computer Graphics, Computer Graphics, Vol. 25, pp. 319-328, 1991.

[6] P. Machado and A. Cardoso: All the Truth about NEvAr, Applied Intelligence, Vol. 16, pp. 101-118, 2002.

[7] J. Rigau and M. Feixas and M. Sbert: Informational Aesthetics Measures, IEEE Computer Graphics and Applications, Vol. 28, No. 2, pp. 24-34, 2008.

[8] E. den Heijer and A. E. Eiden: Using Aesthetic Measures to Evolve Art, Proceedings of the Conference on Evolutionary Computation 2010, pp. 4533- 4540, Barcelona, Spain, 2010.

[9] K. Matkovic and L. Neumann and A. Neumann and T. Psik and W. Purgathofer: Global Contrast Factor - a New Approach to Image Contrast, Computational Aesthetics 2005, pp. 159-168, 2005.

[10] J.-M. Jolion: Images and Benford's Law, Journal of Mathematical Imaging and Vision, Vol. 14, No. 1, pp. 73-81, 2001.

[11] G. K. Zipf: Human behavior and the principle of least effort - an introduction to human ecology, Hafner Pub. Co., New York, 1949.

[12] H. Satoh and I. Ono and S. Kobayashi: A New Generation Alternation Model of Genetic Algorithms and Its Assessment, Journal of Japanese Society for Artificial Intelligence, Vol. 12, No. 5, pp. 734-744, 1997 (in Japanese).

[13] T. Unemi: A Design of Multi-Field User Interface for Simulated Breeding, in Proceedings of the third Asian Fuzzy Systems Symposium, Masan, Korea, pp. 489-494, 1998.


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