Predicting perceived visual complexity of abstract patterns using computational measures

Author(s)
Andreas Gartus, Helmut Leder
Abstract

Visual complexity is relevant for many areas ranging from improving usability of technical displays or websites up to understanding aesthetic experiences. Therefore, many attempts have been made to relate objective properties of images to perceived complexity in artworks and other images. It has been argued that visual complexity is a multidimensional construct mainly consisting of two dimensions: A quantitative dimension that increases complexity through number of elements, and a structural dimension representing order negatively related to complexity. The objective of this work is to study human perception of visual complexity utilizing two large independent sets of abstract patterns. A wide range of computational measures of complexity was calculated, further combined using linear models as well as machine learning (random forests), and compared with data from human evaluations. Our results confirm the adequacy of existing two-factor models of perceived visual complexity consisting of a quantitative and a structural factor (in our case mirror symmetry) for both of our stimulus sets. In addition, a non-linear transformation of mirror symmetry giving more influence to small deviations from symmetry greatly increased explained variance. Thus, we again demonstrate the multidimensional nature of human complexity perception and present comprehensive quantitative models of the visual complexity of abstract patterns, which might be useful for future experiments and applications.

Organisation(s)
Department of Cognition, Emotion, and Methods in Psychology
Journal
PLoS ONE
Volume
12
ISSN
1932-6203
DOI
https://doi.org/10.1371/journal.pone.0185276
Publication date
11-2017
Peer reviewed
Yes
Austrian Fields of Science 2012
501001 General psychology, 501011 Cognitive psychology
Portal url
https://ucris.univie.ac.at/portal/en/publications/predicting-perceived-visual-complexity-of-abstract-patterns-using-computational-measures(eddbf6a3-102b-4b31-bd97-c8b4bed893c9).html