The Computational Creativity Complex - Computational Creativity Research: Towards Creative Machines - page 64

Fig. 4.1 A diagram outlining the two major components of DARCI. Image analysis learns how to
annotate new images with adjectives using a series of appreciation networks trained with labeled
images (outlined in blue ). Image generation uses a semantic memory model to identify nouns and
adjectives associated with a given concept. The nouns are composed into a source image that is
rendered to reflect the adjectives, using a genetic algorithm that is governed by a set of evaluation
metrics. The final product (outlined in red ) is an image that communicates the given concept
to provide adjectives that describe the image. When users input a word with multiple
senses, they are presented with a list of the available senses, along with the WordNet
gloss, and asked to select the most appropriate one. Additionally, for each image
presented to the user, DARCI lists seven adjectives that it associates with the image.
The user is then allowed to flag those labels that are not accurate.
Learning image to synset associations is a multi-label classification problem [ 19 ],
meaning each image can be associated with more than one synset. To handle this,
we use a collection of artificial neural networks (ANNs) that we call appreciation
networks , each of which outputs a single real value, between 0 and 1, indicating
the degree to which a given image can be described by the network's corresponding
synset (adjective). An appreciation network is created for each synset that has a
sufficient number of training data, and as data are incrementally accumulated, new
neural networks are dynamically added to the collection to accommodate any new
synsets. There are currently close to 300 appreciation networks in the system.
Semantic Memory Model The system also contains a simple cognitive model, built
as a semantic network forming a graph of associations between words [ 20 , 21 ]. These
word associations are acquired in one of two ways: from people and by automatic
inference from a corpus, with the idea being to use the human word associations to
capture general knowledge and then to fill in the gaps using the corpus associations.
For the human word associations, we use two pre-existing databases of free asso-
ciation norms (FANs): the Edinburgh Associative Thesaurus [ 22 ] and the University
of Florida's Word Association Norms [ 23 ]. These word associations were acquired