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4.2 Three Blind Mice
Our three specific examples, from which we will try to generalize an abstract sys-
tem are DARCI, a computational artist that creates visualizations for communicating
concepts [ 1 - 5 ]; CARL, a computational composer that discovers musical motifs in
non-musical sources and composes music around them [ 6 , 7 ]; and PIERRE a com-
putational chef that creates original slow cooker recipes [ 8 ]. Each of these systems
has been presented in more detail elsewhere, and we give only enough detail here to
support the generalization that is our goal.
4.2.1 DARCI
DARCI is a system for generating original images that convey intention and is
inspired by other artistic image generating systems such as AARON [ 9 ] and The
Painting Fool [ 10 ]. Central to the design philosophy of DARCI is the notion that
the communication of meaning in art is a necessary part of eliciting an aesthetic
experience in the viewer [ 11 ], and it is unique in that it creates images that explicitly
express a given concept using visual metaphor . This is currently done at two levels:
using iconic nouns as surrogates for the target concept and using image filters to
convey associated adjectival content.
DARCI is composed of two major subsystems, an image analysis component,
and an image generation component. The image analysis component learns how
to associate images with concepts in the forms of nouns and adjectives. The image
generation component composes an original source image as a collage of iconic noun
concepts and then uses a genetic algorithm, governed by the analysis component,
to render this source image to visually convey an adjective. Figure 4.1 outlines this
process of creating artifacts.
4.2.1.1 Image Analysis
DARCI's understanding of images is derived from two sources: a mapping from
low-level image features to descriptive adjectives and semantic associations between
linguistic concepts.
Visuo-Linguistic Association In order for DARCI to make associations between
images and associated adjectives, the system learns a mapping from low-level com-
puter vision features [ 12 - 17 ] to words using images that are hand-labeled with
adjective tags. The use of WordNet's [ 18 ] database of adjective synsets allows images
to be described by their affect, most of their aesthetic qualities, many of their possible
associations, and even, to some extent, by their subject.
To collect training data we have created a public website for training DARCI
( http://darci.cs.byu.edu ) , where users are presented with a random image and asked
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