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Linking Computerized and Perceived Attributes of Visual Complexity

Authors:
Kanaka Babshet
Vered Aharonson

Keywords: Visual complexity; Cognitive assessments; Computer vision; Binary images.

Abstract:
Psychological studies explore visual complexity as perceived by humans. Image complexity is studied extensively in the mathematical, computational sciences. The two disciplines often define visual complexity differently and are thus disjointed. This is manifested in differences between subjective human-perceived complexity, and computer vision algorithms' performance in visual tasks. Our study investigates this discrepancy in the context of cognitive tests that employ visual stimuli to assess a subject's primal cognitive functions. A database of cognitive tests including visual recognition tasks and the performance of 403 subjects in terms of response times was used. Computer vision and information theory features were extracted from the images in these tasks. Inspired by cognitive psychology studies, the features were categorized into whole-image and object-specific features. A random forest classifier was used to map the computed features into three complexity labels in the tasks, labelled according to the subjects' performance. The classifier computationally captured the significant features for the human-perceived task complexity by mapping the occurrence of these features to the complexity labels of the subjects' performance. The whole-image features demonstrated greater visual significance than the object-specific features. The features' importance values could provide insights into the links between mathematical visual complexity definitions and visual complexity as perceived by humans.

Pages: 28 to 33

Copyright: Copyright (c) IARIA, 2020

Publication date: April 26, 2020

Published in: conference

ISSN: 2308-4197

ISBN: 978-1-61208-780-1

Location: Nice, France

Dates: from October 25, 2020 to October 29, 2020