A visual complexity learning algorithm for modelling human performance in visual cognitive tests
Date
2019
Authors
Babshet, Kanaka
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Abstract
Visual complexity has been extensively studied in the mathematical, computational
sciences. Concurrently, psychological studies have attempted to de ne visual complexity
as perceived by humans. The problem lies in that the computational and psychological
studies are always explored separately, and thus their de nitions of visual complexity
are disjointed. This is evident when attempting to capture human-perceived complexity
through computer vision.
This research attempts to tackle this problem in the context of cognitive assessments. This
context introduces a practical application to the general question of computer, and human
perception of complexity: Computerized cognitive assessments regularly employ visual
stimuli, and present tasks that test a subject's primal cognitive functions. The di culty
of these tasks is not objectively quanti ed, which reduces the e ciency of the tests'
administration, and the accuracy of the results' interpretation. This study developed
and examined an algorithm that could computationally predict a visual task's humanperceived
complexity.
The algorithm used a database of visual tasks and subjects' performance in terms of
response times. Human subjective evaluation of tasks' complexity were captured for a
subset of these tasks. Two types of feature sets were extracted from the visual stimuli
presented in the tasks: object-speci c, and whole image features. Several classi ers were
implemented, using the features and the subjects' perceived visual complexity labels. The
best algorithm con guration yielded a 58 % prediction, for a three-class complexity scale.
An analysis of the performance of the algorithm, and the relative visual features' importance
values, provided insights which could help bridge the gap between mathematical
complexity, and human perceived complexity
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Citation
Babshet, Kanaka Sudhir (2019) A visual complexity learning algorithm for modelling human performance in visual cognitive tests, University of the Witwatersrand, Johannesburg, <http://hdl.handle.net/10539/28922>