USING AESTHETIC LEARNING MODEL TO CREATE ART
Yang Li
1
, Chang-Jun Hu
1
and Jing-Qin Pang
2
1
School of Computer and Communication Engineering
University of Science and Technology Beijing, Beijing 100083, China
2
School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, China
Keywords:
Aesthetic learning, Evolutionary art, Interactive evolutionary computation, Computational aesthetics.
Abstract:
An aesthetic learning model is proposed that applies evolutionary algorithm to generate art. The model is
evaluated using an evolutionary art system by human subjects. The advantages of the model is that it helps
user to automate the process of image evolution by learning the user’s preferences and applying the knowledge
to evolve aesthetical images. This paper implements four categories of aesthetic metrics to establish user’s
criteria. In addition to evolutionary images, external artworks are also included to guide evolutionary process
towards more interesting paths. Then we described an evolutionary art system which adopted the aesthetic
model in detail. Last, we evaluate the aesthetic learning model in several independent experiments to show the
efficiency at predicting user’s preferences.
1 INTRODUCTION
Evolutionary Art is originated from the work of
Dawkins (Dawkins, 1986), his Biomorphs program.
Following the earliest ideas of Sims(Sims, 1991) and
Latham(Latham and Todd, 1992), which use lisp ex-
pressions to specify the color of every pixel, a wide
research (see, for example, (Lutton, 2006) (Bentley,
1999) (Machado and Cardoso, 2002)) used Genetic
Programming(GP) to evolve aesthetic images. One of
the significant challenges faces such systems is to find
an appropriate fitness function for guiding the evolu-
tionary process. Nowadays, most systems rely on In-
teractive Evolutionary Computation (IEC), which the
user take the tedious task to make decisions for every
generation. However, this could cause serious prob-
lems, like premature convergence, user fatigue and
several other disadvantages(Takagi, 1998).
Most of the existing systems take a long time to
find interesting images. So users usually lost interest
in exploring design space further. In order to reduce
user fatigue, it is important to prevent stagnation in
evolutionary process, keep user’s interests high and
automate aesthetic judgements. Thus it suffers from
at least two major difficulties. First, it is hard to tell
exactly what metrics that influence individual’s final
This work was supported by China Postdoctoral Sci-
ence Foundation Funded Project and the Centre of Excel-
lence for Research in Computational Intelligence and Ap-
plications (CERCIA) at the University of Birmingham, UK.
aesthetic criteria. Second, the searching space is of-
ten fixed by a certain style, which stuck the exploring
paths in a local optimum. This paperwill proposenew
techniques to solve these two issues.
Considering the above issues, our system is aim
at overcoming the drawbacks of existing systems and
automating the aesthetic judgements. This goal is
achieved by (a)devising several metrics and establish-
ing an aesthetic model for measuring user’s aesthetic
values; (b)introducing external images to the training
samples, which could apply external evaluations and
guide the evolutionary process towards more interest-
ing paths.
Three contributions are made by our work. First,
four categories of metrics have been established for
extracting meaningful aesthetic standards. Second,
both internal IEC images and external images col-
lected from internet are included in training the aes-
thetic model. Third, the model based on a classifier is
introduced to automatically distinguish aesthetic im-
ages. To further manipulate the evolutionary process
easily, intuitive mutation parameters are applied.
Our contributions havebeen tested with several in-
dependent experiments by our system. We monitored
how these metrics evolved over iterations, quantita-
tively influenced the aesthetic model and how the sys-
tem directed the selection process by using the aes-
thetic model. We find that the aesthetic model is effi-
cient for most of the users to model their preferences
and properly classifying high, medium and low val-
174
Li Y., Hu C. and Pang J..
USING AESTHETIC LEARNING MODEL TO CREATE ART.
DOI: 10.5220/0003670001740183
In Proceedings of the International Conference on Evolutionary Computation Theory and Applications (ECTA-2011), pages 174-183
ISBN: 978-989-8425-83-6
Copyright
c
2011 SCITEPRESS (Science and Technology Publications, Lda.)
ued images by learning the interactive run and exter-
nal images selected by user.
The paper is organized as follows: Section 2 be-
gins with an overview of previous work on automatic
fitness assignment and computational aesthetics; in
Section 3 we describe our aesthetic model; follow-
ing with Section 4, which includes a global overview
of the system and the experimental results; finally, in
Section 5 we draw conclusions and point directions
for future research.
2 STATE OF ART
Evolutionary Algorithm has been successfully ap-
plied in the fields of design (Bentley, 1999), art
processing (Bachelier, 2008) and imagery generat-
ing (Ventrella, 2008). Most evolutionary art system
use IEC technology, which is based on subjective
human evaluation (Sims, 1991)(Poli and Cagnoni,
1991)(Lutton, 2006)(Rooke, 2002)(Li et al., 2009). A
thorough survey of application and interface research
on IEC can be found in (Takagi, 1998). However,
the user fatigue is one of the biggest problem in this
domain. Therefore, how to automatic the evolution
poses significant challenges to this field.
Several systems havebeen successfully performed
automatic evaluation in music composition (Papadou-
los and Wiggins, 1999)(Todd and Werner, 1998)(Ma-
naris et al., 2005)(Manaris et al., 2007). While in the
field of evolutionary art, it makes the work more dif-
ficult due to the lack of explicit theory like in music
(for a detailed survey see (Machado et al., 2008)).
Two important issues has been addressed in this
paper to formulate an appropriate fitness function for
aesthetic purposes. First, what kind of metrics or cri-
teria influence the final aesthetic judgement; Second,
the relations among them need to be established to as-
sign aesthetic value.
Taking account of the first issue, the automatic
aesthetic judgement falls into the realm of computa-
tional aesthetics, which applies computational meth-
ods that can make applicable aesthetic decisions in
a similar fashion as humans can (Hoenig, 2005). A
brief historical review of the origins of the term could
be found in (Greenfield, 2005). Birkhoff first formal-
ized the aesthetic metric is the ratio between order and
complexity. This metric is then quantifiedby different
measurements on the following work. The work of
Bense (Bense, 1969) and Jaume (Rigau et al., 2008)
defines the complexity and order from the Shannon’s
InformationTheory(Shannon,1951). However, it still
needs tests to prove its validity, especially the rela-
tions between complexity and order.
Later, expression-based evolutionary art system,
NEvAr (Machado and Cardoso, 2002) applied an au-
tomated fitness assignment, which consider both visu-
ally complexity and processing complexity are very
important factors. The aesthetic value is estimated
through the division of the complexity of visual stim-
ulus and complexity of the percept. The two measure-
ments are implemented by JPEG compression and
fractal compression for capturing the self-similarities
of the image. This formula is tested by ”Test of Draw-
ing Appreciation”, which is a standardized psycho-
logical test to evaluate art aesthetics (Machado and
Cardoso, 1998). The result of the test were surpris-
ingly good.
An empirical aesthetic model has been used by
Brian et al. for evolutionary image synthesis (Ross
et al., 2006). The main metric used in this model
is proposed by Ralph (Ralph, 2006), which is based
on hundreds of fine art and bell curve distribution has
also been found in the color gradients.
The secondissue in automatic aestheticjudgement
is to incorporate aesthetic criteria and the metrics of
image in the evaluation strategy. This is difficult
to overcome, because different people have different
principles of beauty and appreciations of various met-
rics.
The most simple way is to merge them together
by a weighted sum. The work of (Wannarumon et al.,
2008) introduced a hardwired fitness function which
combined several measurements that reflect aesthet-
ics of forms and fractals for jewelry design. This
measurement is based on mathematical foundations
of fractal geometry, chaotic behavior and image pro-
cessing. But fixed fitness function often introduces
bias to the evaluation in evolutionary process.
The first published research which uses machine
learning approaches is the work of (Baluja et al.,
1994),which relied on ANN to alleviate the burden
of users. However, the results turned out to be ”some-
what disappointing” (Baluja et al., 1994). (Ross et al.,
2006) use multi-objective fitness testing to evaluate
the candidate textures according to multiple criteria.
The multi-objective approach to evaluate textures is
proposed in (Ross and Zhu, 2004). A Pareto rank-
ing strategy is used to rank the populations, some di-
versity promoting strategies are then provided to gen-
erate a diversity assortment of solutions (Ross et al.,
2006). The results show that the techniques are ide-
ally suited to texture synthesis.
Recently, Machado et al. present an IEC with
a similarity-based approach in their evolutionary art
system (Machado et al., 2008). An Artificial Art
Critic (AAC) is introduced to distinguish between ex-
ternal images (e.g., paintings) and the internal images
USING AESTHETIC LEARNING MODEL TO CREATE ART
175
created by evolutionary process. Thus it enables new
trends and explorations in stylistic changes.The AAC
includes two models: (i) the feature extractor and (ii)
Artificial Neural Network (ANN). The evaluator is
used to distinguish between the internal and external
images (Machado et al., 2008). (Ekart et al., 2011)
propose a set of aesthetic measures identified through
observation of human selection of images and then
use these for automatic evolution of aesthetic images.
3 AESTHETIC LEARNING
MODEL
Three important issues are addressed in our aesthetic
learning model. First, image metrics which we con-
sidered very important factors in aesthetic judgements
should be calculated in the model. Although different
people might disagree on the meaning of beauty, most
metrics are primary characteristics of visual systems
and user’s interest. Second, training populations are
specifically chosen for the learning classifier. Outer
images are involved to avoid convergency to a spe-
cific style. Besides, inner images generated using
IEC are also collected for training sets in every itera-
tion. Third, decision tree is used to build the learning
model. Applying populations with assigned fitnesses
using the model in the continuous generation is one
way to reduce the users’ fatigue. This is achieved by
learning the metrics that extracted from images and
applying the model to the following evolutionary pro-
cess.
3.1 Aesthetic Metrics
Inspired by the works of (Rigau et al., 2008),
(Schmidhuber, 1997) and (Machado and Cardoso,
1998), we complement the metrics with these theory
by considering these established aesthetic measures.
This work is a continuation of our previous work that
primarily focused on the estimation of image com-
plexity and image order (Li and Hu, 2010). These
metrics that we choose fall into four categories: color
ingredient, image complexity, image order and metric
based on Machado and Cardoso’s work(MC metric).
These categories are shown in Table 1. Additionally,
we divide the original image into five parts to calcu-
late these metrics, because spatial information is able
to help analyze different parts of the image.
We apply these metrics to different parts of the
image and denote them as m
i
(1 i 14), which are
described as follows.
Table 1: Aesthetic Metrics Summary
Category Description
Color Ingredient Average value and standard deviation
of Hue, Saturation and Lightness
Image Complexity Information entropy of HSL, RGB and Y
709
Image Order Kolmogorov’s complexity based on
JPEG and fractal compressor
MC metric Image complexity(IC) and Processing
complexity(PC) based on MC
3.1.1 Color Ingredient
Our motivation for the choice of color ingredient is
based on HSL color space, which is more intuitive
than RGB to describe perceptual color. Hue specifies
the base color, the other two specify the saturation and
the light of the color, three of which are all important
factors addressed in perception. The color channels
we used for the color ingredient metric is hue, satu-
ration and lightness. We converted the RGB data of
each image to HSL color space, producing three ma-
trices I
H
, I
S
, I
L
, each of which the dimension is M×N.
Then we proceed by calculating the average value and
the standard deviation in each of these three channels.
Although a computer may describe a color using
the amounts of red, green and blue, a human prob-
ably define it by its attributes of hue, saturation and
lightness. Hue is the angle in the color wheel, the
first metric is computed as the average value of hue
m
1
=
1
MN
M1
x=0
N1
y=0
I
H
(x, y). Saturation and light-
ness are also calculated in the same way using I
S
and
I
L
separately to get the metrics m
2
and m
3
. The aver-
aged saturation indicator represents the purity of the
color. While light also turns out to be a very impor-
tant factor to discriminate between appealing and un-
appealing images, because sometimes it indicates the
sunlight, shadow or darkness.
In addition, the standard deviation of the three
channels are computed as metrics m
4
, m
5
and m
6
.
These metrics represents the scale of the color dis-
tribution. In the artistic domain, the range of colors in
paintings that selected by the artist is the fundamental
step to produce a fine art.
3.1.2 Image Complexity
Our measurements of image complexity are based on
the concepts of information theory. The estimation
of the metric has been stated in (Li and Hu, 2010).
The relationship between complexity and aesthetics
has been widely discussed (Birkhoff, 1933)(Machado
and Cardoso, 1998) (Rigau et al., 2008). We use the
image complexity measurement from an information-
theoretic perspective for this metric. The reason we
consider informational aesthetics measurement is in
(Rigau et al., 2008).
ECTA 2011 - International Conference on Evolutionary Computation Theory and Applications
176
HSL, RGB and Y
709
channels are used to calcu-
late the information content of the image. The image
complexity for hue channel is computed as follows:
m
7
= N × (
xX
p
hue
(x)logp
hue
(x)). (1)
where p
hue
is the probability distribution in channel
hue, which is calculated as follows:
p
hue
(x) =
n
x
N
(0 x χ). (2)
where n
x
is the number of pixels in bin x.The value
of χ is different according to the channel, which rep-
resents the bins of the histogram. In our case, χ for
hue, saturation and lightness channel are 360, 100 and
100 respectively. The image complexity is calculated
by multiplying each pixel’s Shannon entropy in ev-
ery channel and the number of pixels. m
8
and m
9
are
also computedin the same manner for the information
channel of saturation and lightness.
The complexity is also calculated from RGB rep-
resentation andY
709
, which is the luminance from lin-
ear red, green and blue. In RGB space, we divide it
into 512 cubes with eight equal partitions along each
axis to reduce the calculation in 256
3
dimension. The
intensity histogram is then reduced to 512 X
RGB
bins.
The χ for the channel Y
709
is 256. Probability dis-
tributions of the variables X
RGB
and X
Y
709
are used to
calculate the entropy of RGB and Y
709
channel, which
we denote them as the features m
10
and m
11
.
3.1.3 Image Order
The image order is estimated based on the work
of Schmidhuber (Schmidhuber, 1997). (Li and Hu,
2010) have explored the following hypothesis: an im-
age which can be computed by an algorithm in the
shortest description is considered the most beautiful
in a set of images. The coding algorithm is presented
for the description of the human visual system. In
our case, we use the real-world fractal compressor to
achieve this metric. We choose this image compres-
sion method because it is intended to compress in a
visible way for human eye. Training images are as-
signed by three fitness values. Thus the priori dis-
tribution of the images, logP(i), is assigned to 1,
0.5 or 0 which represents high, medium or low value.
logP(C), the constant when C is given, is disregarded.
According to the definition, the order of the image is
calculated as follows:
logP(i | C) =
CompressionRatio
t
e
t
s
logP(i). (3)
t
e
and ts are the end-time and start-time of fractal en-
coding. In order to calculate this evaluation, we first
average the RGB pixels into grey ones, then the image
is divided into four equal regions. The fractal algo-
rithm encodes each region based on its self-similarity.
Thus the metric m
12
is approximated by the fractal
compression.
3.1.4 MC Metric
This metric is based on the aesthetic theory of
Machado andCardoso (Machado and Cardoso, 1998).
They stated that the aesthetic value of an artwork is
connected to Image Complexity (IC) and Processing
Complexity (PC). Although, they developed a for-
mula, the
IC
PC
ratio, to evaluate the aesthetic value, we
consider both complexities are significant metrics for
the aesthetic model. Therefore, IC and PC are esti-
mated separately as independent metrics.
In order to compute IC, we compress the image
using JPEG compressor. In the compression method,
the image is coded with error, we can specify the
amount of detail kept and the compression ratio.The
IC metric is estimated through the division of the
root mean square error(RMSE) by the compression
ratio resulting from the JPEG compression, m
13
=
RMSE
CompressionRatioJPEG
. Low values of IC indicate sub-
stantially compressible and low complexity of the im-
age. While high values of IC indicate not compress-
ible and more complex image.
PC suppose to reflect the compression process of
the image. The fractal image compression is some-
how closer to the way in which humans process the
images. We use fractal image compression for esti-
mating PC. The image varies in perception process as
the time passes, so CP is different in every moments.
In order to compute PC in different time points t
0
and
t
1
, we measure it separately as PC(t
0
) and PC(t
1
). We
argue that PC can be substituted by the following for-
mula:
m
14
= (PC(t
0
) × PC(t
1
)) × (
PC(t
0
) PC(t
1
)
PC(t
1
)
). (4)
This process yields a total of 14 metrics for the
whole image. However, these global metrics could
be incorrect. To better capture metrics in different
regions of the image, we segment each image into
ve different partitions: four quadrants and an cen-
tral square with the same size. Then we applied the
same 14 metrics to each of partitions and the global
image. Thus a total of 84 features are extracted for
each image.
3.2 Training Set
In order to build the aesthetic model, a training set
of 64× 64 pixel outer and inner images was used.
USING AESTHETIC LEARNING MODEL TO CREATE ART
177
The process used to obtain these images was as fol-
lows. For each generation, one image that the subject
choose as the parent of the next generation, as well
as other 66 images present on the screen, became the
inner candidates for the training set. The subject rank
these images into three categories: low, medium and
high, which are assigned to values 0.0,0.5 and 1.0 re-
spectively. Although collecting the inner images gen-
erated using IEC as the candidates would have been
easier than including the real world artworks, it leads
to specific stylistic images that belong to the EC-
generated class. Therefore, in order to provide the
training set with external evaluations, images which
the subject selected from paintings, photographs, in-
ternet, etc. were required. These outer images are
assigned to 1.0 as they were regarded as the interest-
ing images. Through several interactive evolutions,
hundreds of images were collected in total.
An image library is built to save different artworks
that we collected from internet. During the evolu-
tionary process, the subject can choose any pleasing
images from our image library to adjust the model.
To train the model, images were scaled to range
60× 60. About 30 paintings were collected from dif-
ferent sources. The paintings are from the artists Ed-
vard Munch, Marc Chagall and Vincent Van Gogh.
Paintings from one artist can be chosen to allow a bias
towards a particular artist’s style, or a set of abstract
paintings can be selected to guide the model towards
a specific type.
The inner populationsare gathered during the evo-
lutionary process. The population size of each itera-
tion is 67. The total number of the inner populations
depends on the number of iterations before the evolu-
tionary process stops.
3.3 Learning Aesthetic
As we stated, this model is based on a classifier, the
input of which is the 84 metrics that we introduced
and the fitnesses. The classifier is chosen by the fol-
lowing reasons:(1) we could explore the metrics of
image that are shown correlations with user’s aes-
thetic judgement; (2) images could be rendered with
fitness assigned by the model in each generation in
order to help the user to evaluate populations.
ANN is the mostly used machine learning ap-
proach to learn evaluating aesthetic judgments. Al-
though this approach is elegant, the ANN training
process is usually time-consuming and not easy to un-
derstand. In (Machado et al., 2008), it needs 6 sec-
onds per image to extract features, and ANN train-
ing takes hours to train the model. And in the work
of Baluja, it becomes a very difficult undertaking to
determine what the ANN has ’learned’, and an even
harder task to translate the knowledge embedded in
the ANN into understandable rules (Baluja et al.,
1994). These are the two reasons why we use deci-
sion trees to build the classifier. By looking at the
decision tree, it is easy to see which variables split the
data into aesthetic category. This information is very
important for us to understand the nature of beauty
according to the user’s behavior.
Therefore, decision tree is one utility that could
help us better understand the influence of different
metrics and criterias directly. The structured rules that
learned from the model would be applied on the im-
ages to assist in making aesthetic decisions. In order
to build the classify model, we focus on C4.5 (Quin-
lan, 1993) decision tree to analyze the users’ behavior
using WEKA J48 (Witten and Frank, 2000) imple-
mentation.
In our paper, we convert our data collected from
evolutionary process into a file, which is the input
for the algorithm. 84 metrics are the attributes with a
set of different real values in several evolutionaryrun.
Then the algorithm of the decision tree recursively di-
vides the dataset into smaller subsets by selecting one
of the most informative attribute until it has been la-
beled as terminal. The model is then constructed by
the decision tree, which will be applied in the new
evolutionary process. It is used to classify new popu-
lations into three categories to help users accomplish
the primary evaluation.
4 EXPERIMENTS
In this section, we define what kind of evolutionary
art system we use for the interactive evolution, how
we conduct our experiments to evaluate the aesthetic
model, and what subjects participated in our exper-
iments. After that, we present an individual interac-
tive evolutionexperiment to better understand the aes-
thetic judgement process, use the automatic model to
compare the results, and discuss the effectiveness of
the aesthetic model by performing a number of runs
for different subjects.
4.1 System Description
Our system is comprised of two modules, image gen-
erator which uses GP to produce images and aesthetic
model. The overall architecture of our system is given
in Figure 1. The method we employed in image gen-
erator is described in Section 4.1.2. And the aesthetic
model is briefly introduced in Section 3.
ECTA 2011 - International Conference on Evolutionary Computation Theory and Applications
178
Figure 2: Main window of our system.
Figure 1: Overall architecture of our system.
4.1.1 Overall Architecture
Our evolutionary art system has four distinctive char-
acteristics. First, we setup four intuitive mutation pa-
rameters for user to manipulate the evolutionary pro-
cess. Second,training samples for the aesthetic model
are collected from every generation performed by the
user, while images that didn’t participate in the evo-
lutionary process are also included. This approach
not only enables more stylistic training images intro-
duced in learning process, but also make it more eas-
ier to train the model by applying the domestic im-
ages. Third, four categories of metrics are extracted
from the training samples. Finally, the decision tree is
introduced to build the model, and further assist user
in assigning fitnesses for the continuous generation.
The main window of the system is shown in Fig-
ure 2. Initial populations generated randomly by GP
are displayed on the screen. From the displayed set
the user can assign fitness by choosing one of the
three icons below each image, which represents high,
medium or low value. Besides the IEC images, outer
images also can be chosen from our painting library
during the evolutionary process, which are automati-
cally assigned high fitness values. Four mutation pa-
rameters can be set manually to adjust rate of stylistic
changing. Evolutionary process stops when the user
meets his/her final criteria or he/she loses interest in
exploring further populations. AutoFitness checkbox
is used to collect the data extracted from the previ-
ous process, learn the user’s aesthetic judgement and
build the aesthetic model.
4.1.2 Image Generator
Genetic Programming
Like most of the evolutionaryart system, the individu-
als in our system are represented by trees, this shares
many similarities with the application developed by
Sims. As such, the genotypes are mathematical func-
tions represented by trees, which are constructed by
a lexicon of functions and terminals. The functions
we use are a set of simple functions, such as tran-
scendental functions, arithmetic operators and logic
operations. The terminals are variables x, y, or con-
stants. And then it is normalized into the proper range
to specify the color of the pixels.
The symbolic expression is called for every pixel
of the image to calculate the color. There are two
kinds of root nodes to map the values into color. One
is RGB node, which is applied by three channels, red,
green and blue to determine the final color. Another
one is color map node. The numeric value is called by
this node, which is then looked up in a color map to
transfer into the real color. In Figure 3 we present
some examples of genotype and its corresponding
phenotypes.
Mutation Operators
Mutation operators are one of the most important ge-
netic operators for the stylistic variations in images.
USING AESTHETIC LEARNING MODEL TO CREATE ART
179
Figure 3: Some examples of genotypes and their corre-
sponding phenotypes.
Figure 4: Some examples of generations according to dif-
ferent mutation operators.
Mutation is performed by changing values in a leaf
node or functions in an internal node, deleting the
subtree at any point or replacing the pruning part
with a new random subtree (Li and Hu, 2010). Four
kinds of mutation operators are applied in our system,
which are coarse mutation rate, color mutation rate,
pattern mutation rate and fine mutation rate. They are
used to control the rate of changing the subtree, the
internal node, the root node and the leaf node. In Fig-
ure 4, the parent is the image on the top of the fig-
ure, examples of mutation populations generated by
different mutation operators with corresponding mu-
tation rates are shown below. Although populations
are not precisely transformed by using each mutation
operator, they can lead to specific exploration accord-
ing to users’ preference.
4.1.3 Evolutionary and Learning Process
The evolutionary and learning process is briefly de-
scribed as following steps:
1. A set of initial GP images are displayed on our
system.
2. The subject choose his/her favorite images from
the image library that we now collected from fa-
mous paintings on internet.
3. Fitness values of current populations are assigned
by selecting one of the three icons below each im-
age according to his/her preference. The one with
high fitness is selected as the parent of next gener-
Table 2: Parameters of GP for representation.
Parameter Setting
Population size per generation 67
Mutation operator coarse, color, pattern and fine mutation
Mutation Rate manually set every generation, in the range 0-100
Unary function set sin, cos, tan, abs, floor, ceil, sqr,
square, cube, log, exp, negative, spiral, circle
Binary function set +,-,*,/, max, min, pow, average
Ternary function set if, lerp
Terminal set X,Y, scalar and random constants
Initial maximum tree depth 6
ation. Four mutation parameters are also set man-
ually.
4. Aesthetic metrics are extracted from the cur-
rent populations and favorite paintings, and then
stored in a measure list.
5. The evolutionary process stops when a termina-
tion criteria is met. In our case, the number of
generations is fixed or the subject choose to train
the aesthetic model.
6. The training result is shown on screen, according
to which the subject can choose continue training,
start over or use the model to continue generating.
7. The model is used to assign fitnesses in the fol-
lowing generations.
4.2 Experimental Setup
In this section we present some of the experimental
results applied with our aesthetic model.
Our intention in implementing this system is not
to exclusively reduce errors in learning aesthetic but
to decrease the tedious work in IEC process, and try
to assist user to interactively evolve images that they
find aesthetically pleasing. For this purpose, we are
mainly interested in analyzing the metrics relevant to
aesthetic judgement, reducing user fatigue and cate-
gorizing the populations to help preliminary evalua-
tion. Therefore, we first conducted one single exper-
iment, from the first IEC generation till the last itera-
tion that performed by the model we built. Then we
completed 42 runs on our system by 21 different sub-
jects, two experiments by each subject.
In the experiments we use the settings presented in
Table 2. 67 images are displayed in every generation,
all of which are rendered at 64 by 64 resolution. The
functions that we use in GP fall into three categories:
unary, binary and ternary functions. The terminals
are variable x, y position or constants. The maximum
initial tree depth is set to 6.
4.3 Analysis of the Model
We conducted one experiment from random popula-
tions to the last generation evolved by our aesthetic
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180
Figure 5: Initial populations of the experiment. The icon
below each image indicate the individual’s fitness value.
model. In this single experiment, the subject manip-
ulated 10 generations. IEC images generated from it-
eration to iteration, while eight paintings chosen from
our image library remains the same. The number of
the outer images still need to be adjusted in the future
work to solve the unbalanced classes problem.
Initial Populations
The initial populations of our system is shown in Fig-
ure 5. The population size of the initial random im-
ages is 67. These populations were created using ran-
dom tree depths to avoid monotony of the images.
Eight external paintings were also selected by user
which we collected from internet. The mutation rates
for the next generation were then set separately. The
third image from the upper-left corner of the figure
was selected as the parent. Parent from every itera-
tion is kept in the next generation, thus genotype with
high fitness value occurs more than once in the evo-
lutionary process. In order to remove the repetition
in the aesthetic model, we delete the same population
that appears in the previous iteration in the training
set.
IEC Results
Considering the main goal of our work, preventing
stagnation in evolution and keeping users’ interests
high is one way to reduce user fatigue. Users usu-
ally lost interest in exploring the design space fur-
ther, because most of the systems produce imagesthat
are quite similar to each other or the process leads to
blind exploration. Therefore, we applied four muta-
tion operatorsto better manipulate the exploring paths
in search space.
In the experiment the subject performed the ex-
periment as we described before. The mutation rates
were set manually, as shown in Figure 6. We present
the experimental results attained in the 5th and the last
generation in Figure 7 and Figure 8. The results show
that from the first iteration till the last, user could
easily control the convergence of optimum or main-
tain sufficient diversity in exploring the design space.
0 2 4 6 8 10 12
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Mutation Rate
Iteration t
Mutation Rate
Coarse Mutation
Color Mutation
Pattern Mutation
Fine Mutation
Figure 6: Mutation Rate values along with iterations.
Figure 7: IEC populations from the 5th iteration.
Figure 8: IEC populations from the 10th iteration.
Most of the populations in 5th iteration generates the
pattern of a flower. In the following iterations, the
user mainly focused on exploring the color and slight
variation in shape.
Training Results
Another method to reduce the evaluation work is to
build the aesthetic model to learn the users’ prefer-
ences. Our aesthetic model first extracted metrics that
we stated before from IEC populations and outer im-
ages, then the training results is shown on screen (see
Figure 9). All the trainingresults, includes roc curves,
decision tree and classified rate are displayed. The
user decide whether to continue training, start over or
apply the model to the following evolutionary runs.
Table 3 shows the accuracy of the correctly classi-
fied images with high, medium and low value, which
USING AESTHETIC LEARNING MODEL TO CREATE ART
181
Table 3: Number of image and correctly classified images.
Number of image in training set Correctly classified Rate
Low 30 73.33%
Medium 426 80.75%
High 345 83.19%
Figure 9: Training results after building aesthetic model.
Figure 10: Populations from 30th iteration using the aes-
thetic model.
we used a 10-fold cross validation. The results show
that our system is capable of capturing user’s prefer-
ence from evolutionary process. This is very impor-
tant for trying to reduce the user’s fatigue. The model
is applied to help filter out most of the uninteresting
populations, thus brings images satisfied users.
The results generated by the model for the follow-
ing evolutionary runs are shown in Figure10. We find
that the aesthetic model is able to distinguish three
categories of images. Most of the low value images
are correctly classified by the model. And the popula-
tions in final iteration share some similarities in stylis-
tic and color with the results generated by IEC.
4.4 Subjects Evaluation
We request 21 students in twenties to operate our sys-
tem, each of which runs twice, with and without aes-
thetic model. In the first experiment, the subjects per-
formed our system like using most of the evolutionary
art system. In the second experiment, the subject used
the same populations in the 10th iteration generated
in previous experiments (we request subjects perform
more than 10 iterations in IEC), then used the training
model to generate next 30 iterations and report the fit-
ness categories of the final images for comparison.
Table 4: average test results of 21 subjects on the aesthetic
model.
Mode Total populations Number of Time Percentage of
populations generations consumption high valued images
IEC 884.4 13.2 10’48“ 43.07%
IEC
(Aesthetic Model) 3189.2 47.6 25’21“ 63.5%
Table 4 shows the average time consumption,
number of populations, number of generations and
percentage of high valued images made by human
subjects. Clearly, it took 2/3 of the time used by
IEC in each iteration using aesthetic model. In other
words, more images are generatedin certaintime with
aesthetic model. For IEC with aesthetic model, we
also find that the percentage of high valued images
is increased although the total number of generation
is almost five times more than IEC process. These
results show that our aesthetic model is sufficient to
predict the user’s preference and reduce user’s fatigue
in the evolutionary process.
5 CONCLUSIONS AND FUTURE
WORK
This paper introduce an aesthetic model in exploring
user’s behavior of evaluation in evolutionary art sys-
tem. The model has incorporated several new ideas
in reducing user’s fatigue. The metrics we extracted
from images are fall into four categories, color ingre-
dient, image complexity, image order and MC met-
ric. Four new features has significantly improved our
system for stylistic changing and better exploration.
External evaluations of real world paintings or pho-
tographs help the model involving outside values of
metrics not only limited in IEC space. The four muta-
tion operators can help the system to avid stagnation
and blind exploration.
Although more rigorous and more comprehensive
evaluation of our model is needed, our preliminary
study here does illustrate the efficiency of out sys-
tem. The future work of this research includes sev-
eral tasks. First, comparison of different metrics help
us to better understand aesthetic criterias in evolution-
ary process. Second, exploring the relations between
the inner IEC evaluations and external evaluations for
outer images may also help to analyze the aesthetic
model.
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182
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