Low-complexity art

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Jürgen Schmidhuber: Low-complexity art. Leonardo, 97–103 (1997). JSTOR



Many artists when representing an object try to convey its ``essence." In an attempt to formalize certain aspects of depicting the essence of objects, the author proposes an art form called low-complexity art. It may be viewed as the computer-age equivalent of minimal art. Its goals are based on concepts from algorithmic information theory. A low-complexity artwork can be specified by a computer algorithm and should comply with two properties: (1) the drawing should ``look right," and (2) the Kolmogorov complexity of the drawing should be small (the algorithm should be short) and a typical observer should be able to see this. Examples of low-complexity art are given in the form of algorithmically simple cartoons of various objects. Attempts are made to relate the formalism of the theory of minimum description length to informal notions such as ``good artistic style" and ``beauty."

Extended Abstract


author={Jürgen Schmidhuber},
journal={Leonardo, Journal of the International Society for the Arts, Sciences, and Technology},
title={Low-complexity art},
editor={MIT Press}, 
url={ftp://ftp.idsia.ch/pub/juergen/locoart.ps.gz http://people.idsia.ch/~juergen/locoart/locoart.html http://de.evo-art.org/index.php?title=Low-complexity_art},

Used References

1. M. Li and P.M.B. Vitanyi, An Introduction to Kolmogorov Complexity and Its Applications (Springer-Verlag, New York, 1993). Page v in preface.

2. A.N. Kolmogorov, ``Three Approaches to the Quantitative Definition of Information," Problems of Information Transmission 1 (1965) pp. 1-11; G.J. Chaitin, ``On the Length of Programs for Computing Finite Binary Sequences: Statistical Considerations," Journal of the ACM 16 (1969) pp. 145-159; and R.J. Solomonoff, ``A Formal Theory of Inductive Inference, Part 1," Information and Control 7 (1964) pp. 1-22.

3. See Li and Vitanyi [1] for the best overview. See J. Schmidhuber, ``Discovering neural nets with low Kolmogorov complexity and high generalization capability", Neural Networks (1997, in press), for a machine learning application. A short version of this text has appeared in A. Prieditis and S. Russell, editors, Machine Learning: Proceedings of the Twelfth International Conference, pages 488-496, Morgan Kaufmann Publishers, San Francisco, CA, 1995.

4. See Kolmogorov [2]; Chaitin [2]; and Solomonoff [2].

5. See Kolmogorov [2]; Chaitin [2]; and Solomonoff [2].

6. B. Mandelbrot, The Fractal Geometry of Nature (San Francisco: Freeman, 1982).

8. See Kolmogorov [2]; Chaitin [2]; and Solomonoff [2]; L.A. Levin, ``Laws of Information (Nongrowth) and Aspects of the Foundation of Probability Theory," Problems of Information Transmission 10, No. 3, 206-210 (1974); C.S. Wallace and D.M. Boulton, ``An Information Theoretic Measure for Classification," Computer Journal 11, No. 2, 185-194 (1968); and J. Rissanen, ``Modeling by Shortest Data Description," Automatica 14 (1978) pp. 465-471.

9. J.H. Langlois and L.A. Roggman, Attractive Faces Are Only Average, Psychological Science 1 (1990) pp. 115-121.

10. D.I. Perrett, K.A. May and S. Yoshikawa, ``Facial Shape and Judgments of Female Attractiveness," Nature 368 (1994) pp. 239- 242.

11. P.J. Benson and D.I. Perrett, ``Extracting Prototypical Facial Images from Exemplars," Perception 22 (1993) pp. 257-262.

12. Lyonel Feininger, translated by J. Schmidhuber.

13. See Mandelbrot [6]; and H.-O. Peitgen and P.H. Richter, The Beauty of Fractals: Images of Complex Dynamical Systems (Springer-Verlag, New York, 1986).

14. K. Culik and J. Kari, ``Parallel Pattern Generation with One-Way Communications," in J. Karhumaki, H. Maurer and G. Rozenberg, eds., Results and Trends in Theoretical Computer Science (Springer-Verlag, New York, 1994) pp. 85-96.

15. For work on face animation, see I.A. Essa, T. Darrell and A. Pentland, ``Modeling and Interactive Animation of Facial Expressions Using Vision," Technical Report, MIT Media Lab Perceptual Computing Section TR 256, 1994. See also Mengxiang Li, ``Minimum Description Length Based 2-D Shape Description," in IEEE 4th Int. Conference on Computer Vision (May 1992) pp. 512-517.

16. C.E. Shannon, ``A Mathematical Theory Of Communication (Parts 1 and 2)", Bell System Technical Journal 27 (1948) pp. 379-423.

17. F. Nake, Ästhetik als Informationsverarbeitung (Springer-Verlag, Berlin, 1974).

18. See Kolmogorov [2]; Wallace and Boulton [8]; and Chaitin [2].


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