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by appropriate colour harmony theories since if such supervision is not applied,
the search results are liable to be dull and uncoordinated...” Others have applied
a variation of Birkhoff's aesthetic measure for colour harmony attempting to better
define order in colour schemes (Li and Zhang
2004
).
But overall there has been little progress in automating design principles for aes-
thetic evaluation. Feature extraction figures heavily in this problem, so perhaps fu-
ture computer vision researchers will take on this problem.
10.2.3 Artificial Neural Networks and Connectionist Models
Artificial neural networks
are software systems with designs inspired by the way
neurones in the brain are thought to work. In the brain neurone structures called
axons
act as outputs and
dendrites
act as inputs. An axon to dendrite junction is
called a
synapse
. In the brain, electrical impulses travel from neurone to neurone
where the synaptic connections are strong. Synapse connections are strengthened
when activation patterns reoccur over time. Learning occurs when experience leads
to the coherent formation of synapse connections.
In artificial neural networks virtual neurones are called
nodes
. Nodes have multi-
ple inputs and outputs that connect to other nearby nodes similar to the way synapses
connect
axons
and
dendrites
in the brain. Like synapses these connections are of
variable strength, and this is often represented by a floating point number. Nodes
are typically organised in layers, with an input layer, one or more hidden layers, and
finally an output layer. Connection strengths are not manually assigned, but rather
“learned” by the artificial neural network as the result of its exposure to input data.
For example, a scanner that can identify printed numbers might be created by
first feeding pixel images to the input layer of an artificial neural network. The data
then flows through the hidden layer connections according to the strength of each
connection. Finally, one of ten output nodes is activated corresponding to one of
the digits from “0” to “9”. Before being put into production the scanner would be
trained using known images of digits.
Some of the earliest applications of neural network technology in the arts con-
sisted of freestanding systems used to compose music (Todd
1989
). Later in this
chapter artificial neural networks will be described as providing a component in
evolutionary visual art systems (Baluja et al.
1994
).
A significant challenge in using artificial neural networks is the selection, condi-
tioning, and normalisation of data presented to the first layer of nodes. It was noted
in Sect.
10.2.1
that ranked music information following Zipf's law can be used to
identify composers and evaluate aesthetics. Manaris et al. (
2005
;
2003
) reported an
impressive success rate of 98.41 % in attempting to compute aesthetic ratings within
one standard deviation of the mean from human judges.
A similar effort was made to evaluate a mix of famous paintings and images
from a system of evolved expressions. The machine evaluation used Zipfian rank-
frequency measures as well as compression measures as proxies for image complex-
ity. The authors reported a success rate of 89 % when discriminating between human
