The Computational Creativity Complex - Computational Creativity Research: Towards Creative Machines - page 66

concepts that together represent the whole, the idea being that in many cases, if a
(sub)concept is simple enough, it can be represented visually with a single icon.
To represent these “simple enough” concepts, DARCI makes use of a collection of
icons provided by The Noun Project [ 26 ]. Given such a collection of iconic concepts,
DARCI composes their visual representations (icons) into a single image.
When given a concept, DARCI first uses the semantic memory model to retrieve all
words associated with the given concept, including itself. These word associations are
filtered by returning only nouns for which DARCI has icons and adjectives for which
DARCI has appreciation networks. The nouns are sorted by association strength and
the top few are chosen as a collective iconic representation of the concept. These
icons are scaled to between 25 and 100 % of their original size according to their
association strength rank.
An initial blank white image is created, and the set of scaled icons are drawn
onto the blank image at random locations, the only constraints being that no icons
are allowed to overlap and no icons are allowed to extend beyond the border of the
image. The result is a collage of icons that collectively represent the original concept.
DARCI then probabilistically (weighted by each adjective's association strength)
selects an adjective from the set returned by the semantic memory model and then uses
its image rendering component to render the collage image according to the selected
adjective. The final image will both be artistic and in some way communicate the
concept to the viewer.

Image Rendering . DARCI uses an evolutionary mechanism to render images so that
they visually express the meaning of given synsets. The genotypes that comprise each
gene pool are lists of filters (and their accompanying parameters) for processing a
source image, similar to those found in Adobe Photoshop and other image editing
software. The processed image is the phenotype.
Every generation of the evolution, each phenotype is created from the same source
image. The function used to evaluate the fitness of each phenotype created during
the evolutionary process can be expressed by the following equation:

f P f P Fitness ( ) = ʻ A A ( ) + ʻ S S ( P ) (4.1)

where P is the phenotype image and f P

is the vector of image features for a given

F P

phenotype, and A are two functions for modeling

appreciation and similarity, respectively. These functions compute a real-valued score
for a given phenotype (here, F P represents the set of all phenotype feature vectors,
I represents the set of all images and

: ₒ[ 0 , 1 ] and S : I ₒ[ 0 , 1 ]

1).
The appreciation function A is computed as the weighted sum of the output(s)
of the appropriate appreciation network(s). The similarity function S borrows from
research on bag-of-visual-word models [ 27 , 28 ] to analyze local image features (in
contrast to the global features represented by f P ). A bag-of-visual-words is created
for the source image and for the phenotype image, and the Euclidean distance between
their vector representations is calculated. This effectively measures the number of

ʻ A + ʻ S =