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fractal dimension of his paintings increases over time from 1.12 in 1945 to 1.72
in 1952. Presumably Pollock's innovative “dripping” technique improved over time
and in this very limited realm the fractal dimension can be used for aesthetic evalua-
tion (Taylor
2006
). Use of a related measure applied to non-fractal two-dimensional
patterns correlates well with beauty and complexity as reported by human subjects
(Morietal.
1996
).
Work has been done in the fields of medical reconstructive and cosmetic surgery
to quantify facial and bodily beauty as an objective basis for evaluating the results of
medical procedures. Hönn and Göz (
2007
) in the field of orofacial orthopaedics cite
studies indicating that infants preferentially select for facial attractiveness, and that
such judgements by adults are consistent across cultures. Atiyeh and Hayek (
2008
)
provide a survey for general plastic surgery, indicating a likely genetic basis for the
perception of both facial and bodily attractiveness. Touching on rules of proportion
used by artists through the centuries they seem ambivalent or even supportive of
the Golden Ratio standard. However, in conclusion they write, “The golden section
phenomenon may be unreliable and probably is artifactual”.
To date when it comes to quantifying human facial and bodily beauty there is no
medical consensus or standardised measure. More broadly, many now feel that any
simple formulaic approach to aesthetic evaluation will be inadequate. Beauty seems
to be too multidimensional and too complex to pin down that easily.
10.2.2 Design Principles
Another source of aesthetic insight is the set of basic principles taught in typical
design foundations courses. A standard text in American classrooms includes con-
siderations such as: value and distribution; contrast; colour theory and harmony;
colour interaction; weight and balance; distribution and proportion; and symmetri-
cal balance. Also included are Gestalt-derived concepts like grouping, containment,
repetition, proximity, continuity, and closure (Stewart
2008
).
However, to date there is very little in the way of software that can extract these
features and then apply rule-of-thumb evaluations. Among the few is a system that
makes aesthetic judgements about arbitrary photographs. Datta et al. (
2006
;
2007
)
began with a set of photos from a photography oriented social networking site. Each
photo was rated by the membership. Image processing extracted 56 simple measures
related to exposure, colour distribution and saturation, adherence to the “rule of
thirds,” size and aspect ratio, depth of field, and so on. The ratings and extracted
features were then processed using both regression analysis and classifier software.
This resulted in a computational model using 15 key features. A software system
was then able to classify photo quality in a way that correlated well with the human
ratings.
Some work has been done using colour theory as a basis for machine evaluation.
Tsai et al. (
2007
) created a colour design system using genetic searching and noted,
“. . . auto-searching schemes for optimal colour combinations must be supervised
