Poster Abstracts of Computational Aesthetics in Graphics, Visualization, and Imaging (2011)
B. Riecke (Editor)

SBArt4 – Breeding and Evolving Abstract Images andAnimations
T. Unemi
Department of Information Systems Science, Soka University, Japan

Abstract
Some extensions of computational aesthetic measures are proposed for a domain of evolutionary art in order to beembedded in a simulated breeding system, SBArt. In our experiences of the combination of breeding and evolution,
it is effective to reduce the time for producing interesting abstract images and animations.
Categories and Subject Descriptors (according to ACM CCS):
Generation—

I.3.3 [Computer Graphics]: Picture/Image

1. Introduction

2. Aesthetic measures

Interactive Evolutionary Computation (IEC) [Tak01] is a
powerful framework to explore in a huge search space efficiently to find better solutions based on subjective criteria.
K. Sims [Sim91] is a pioneer of this field who developed asystem that allows the user to breed interesting abstract images utilizing a functional expression as genotype. However,the user is often requested to repeat inefficient operations forselection among a number of candidates.

We mention criteria for the layer of perception here but notrecognition to avoid a large scale of knowledge base in a cultural context. It will be helpful enough for efficient breeding since it eliminates uninteresting individuals. We introduce three types of measures for spacial arrangement and
two types for color variation as follows.

Computational aesthetic measure is useful to calculate fitness values in evolutionary computation to automatically obtain beautiful images. As one of the methods to reduce auser’s fatigue of IEC, a combination of automated evolution
based on a type of computational aesthetic measures was examined by P. Machado et al [MC02]. Several different typesof measures were also examined for automated evolution by
E. den Heijer et al [dHE10].
In the research presented here, we have examined to embed an automated evolution into a breeding system for abstract images, SBART, developed by the author [Une09], ofwhich first release was in 1994. The system is using a functional expression for a genotype similarly to many other related works that calculates a color value for each pixel topaint a rectangular area. Some types of measures newly developed are introduced for fitness evaluation combined withmeasures borrowed and modified from previous works. In
addition to measures for a still image, a criterion for animation is also examined for a new functionality to produce ashort animation [Une10].

Copyright is held by the author / owner(s).
CAe 2011, Vancouver, British Columbia, Canada, August 5 – 7, 2011.
ISBN 978-1-4503-0908-0/11/0008

Information theoretic complexity [RFS08] is the ideal
concept to measure how meaningful information the datacontain, but the computation of this measure is not always
able to be completed correctly within a feasible time andspace. We use JPEG compression for an approximation, assame as previous works. Because the ideal value of compression ratio should be a subject to change depending on
the user’s preference, we developed a graphical user interface (GUI) that allows the user to adjust it.
Global contrast factor [MNN∗ 05] is an alternative measure for interesting pattern, of which algorithm was originally designed to evaluate contrast as similarly to human’s
intuitive measure as possible. To expand it to a color space,the difference between brightness is changed into the Euclidean distance between color values in RGB space. Threecomponents are weighted in 2 : 3 : 1 for red, green and blue
to adapt to the characteristics of human eyes.
One dimensional distribution of brightness is newly introduced to consider two dimensional pattern by comparing
the variances of brightness distribution between rows andcolumns. If the pattern is of horizontal stripes, the varianceamong rows is large, but the variance among columns is

T. Unemi / SBArt4zero. The algorithm calculates the distances to the ideal distributions for each angle from 0◦ to 90◦ stepping by 15◦ ,transforms each result from [0, ∞] to [1, 0], then takes a geometric mean among them. The ideal distribution was extracted based on a statistical analysis over 500 snapshot photos similarly to the case of color histogram.
Color histogram is an extended version of the frequency
distribution of brightness introduced by E. den Heijer et al.
To extend the method applicable to colors, we divide thecolor space into 6 × 9 × 3 for each component of red, green
and blue, 162 clusters in total. Instead of well-known distributions, such as Benford’s law and Zipf’s law, we investigated the average distribution among 500 snapshot photosof natural and urban sceneries and portraits. We embedded asequence of frequencies in top 45 colors that almost follows
a mixture of Zipf’s law and exponential distribution.
Favorable distribution of saturation is introduced to reflect the user’s preference of color tones. A GUI of the system allows the user to indicate the ideal values of averageand standard deviation on saturation values over all pixels.
A gray image becomes preferable if both of the values are
low, and a colorful image becomes preferable if the average
is high. The measure is the two dimensional Euclidean distance between the ideal values and the actual values.
It is necessary to normalize the five measures describedabove because they are in different dimensions for each.
We transform each of them into the range of [0, 1] from theworst to the best, so that the distributions are as same aspossible. The transforming equation we designed is f (x) =logm x̄max(0, min(1, 0.2− µ) + 0.5)), where m is the meσ (xdian, and µ and σ are the average and the standard deviation
after exponential transformation. To determine these coefficients, we examined 1,000 images drawn with randomly
generated genotypes.
Aesthetic measure for animation is to measure the distanceto the favorable amount of motion in the animation. It isideal to evaluate the whole duration, but it needs much ofcomputational cost. We implemented an algorithm to calculate the average difference in two consecutive frames amongten samples evenly picked up from the whole sequence. The
measure for a still image is also applied to each sample.
The total evaluation is calculated as the weighted geometricmean between the average measure of still images and theaverage amount of motion in sampled frames. The weight isalso adjustable by the user using a slider in GUI.
3. Evolutionary process
We chose Minimal Generation Gap (MGG) model [SOK97]
for generation alternation because of the quick response andefficiency. The evolutionary process is dynamically illustrated in GUI with a trend graph of the best and averagefitness and a collection of small views for the best 20 phenotypes. It is easy for the user to exchange the individual

genotypes between the population in automated evolution
and the field for simulated breeding by copy & paste anddrag & drop operations.
Automated evolution is useful to produce a set of interesting individuals for the start point of breeding. The best one
may not be user’s favorite but it is easier to find candidatesof ancestors than the case of random individuals. Iteration ofbreeding and evolution is useful to produce interesting, andsometimes beautiful, pieces efficiently.
4. Future works
There are remaining points to be considered in our future research. (1) Statistic analysis of measures concerning correlation and sensitivity should be conducted to clearify how eachof the measures is important. (2) Here we employed a geometric mean because the measures should contribute as necessary conditions. The methods of fuzzy logic to calculate amembership value between logical conjunction is one of thealternatives. Some of the users might prefer to use more than
two measures as sufficient conditions. (3) Two dimensional
Fourier transformation and analysis of the resulted spectra
may one of the useful measures for a still image. (4) Opticalflow for animation and the distribution of flow vectors is alternative measure for animation. These ideas look useful but
it should be checked if they are feasible in computation timefor our purpose.
References[dHE10] DEN H EIJER E., E IDEN A. E.: Using aesthetic measures
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[MC02] M ACHADO P., C ARDOSO A.: All the truth about NEvAr.
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[MNN∗ 05] M ATKOVIC K., N EUMANN L., N EUMANN A., P SIKT., P URGATHOFER W.: Global contrast factor - a new approachto image contrast. In Computational Aesthetics 2005 (2005),
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[RFS08] R IGAU J., F EIXAS M., S BERT M.: Informational aesthetics measures. IEEE Computer Graphics and Applications 28,2 (2008), 24–34.
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[Une09] U NEMI T.: Simulated breeding: A framework of breeding artifacts on the computer. In Artificial Models in Software,
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[Une10] U NEMI T.: SBArt4 - breeding abstract animations in realtime. In WCCI 2010 IEEE World Congress on Computational
Intelligence (Barcelona, Spain, July 2010), pp. 4004–4009.