Multi-scale structural complexity as a quantitative measure of visual complexity

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Autori principali:	Kravchenko, Anna, Bagrov, Andrey A., Katsnelson, Mikhail I., Dudarev, Veronica
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Physics and Society Computer Vision and Pattern Recognition Human-Computer Interaction
Accesso online:	Acceder al recurso
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author	Kravchenko, Anna Bagrov, Andrey A. Katsnelson, Mikhail I. Dudarev, Veronica
author_facet	Kravchenko, Anna Bagrov, Andrey A. Katsnelson, Mikhail I. Dudarev, Veronica
contents	While intuitive for humans, the concept of visual complexity is hard to define and quantify formally. We suggest adopting the multi-scale structural complexity (MSSC) measure, an approach that defines structural complexity of an object as the amount of dissimilarities between distinct scales in its hierarchical organization. In this work, we apply MSSC to the case of visual stimuli, using an open dataset of images with subjective complexity scores obtained from human participants (SAVOIAS). We demonstrate that MSSC correlates with subjective complexity on par with other computational complexity measures, while being more intuitive by definition, consistent across categories of images, and easier to compute. We discuss objective and subjective elements inherently present in human perception of complexity and the domains where the two are more likely to diverge. We show how the multi-scale nature of MSSC allows further investigation of complexity as it is perceived by humans.
format	Preprint
id	arxiv_https___arxiv_org_abs_2408_04076
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	Multi-scale structural complexity as a quantitative measure of visual complexity Kravchenko, Anna Bagrov, Andrey A. Katsnelson, Mikhail I. Dudarev, Veronica Physics and Society Computer Vision and Pattern Recognition Human-Computer Interaction While intuitive for humans, the concept of visual complexity is hard to define and quantify formally. We suggest adopting the multi-scale structural complexity (MSSC) measure, an approach that defines structural complexity of an object as the amount of dissimilarities between distinct scales in its hierarchical organization. In this work, we apply MSSC to the case of visual stimuli, using an open dataset of images with subjective complexity scores obtained from human participants (SAVOIAS). We demonstrate that MSSC correlates with subjective complexity on par with other computational complexity measures, while being more intuitive by definition, consistent across categories of images, and easier to compute. We discuss objective and subjective elements inherently present in human perception of complexity and the domains where the two are more likely to diverge. We show how the multi-scale nature of MSSC allows further investigation of complexity as it is perceived by humans.
title	Multi-scale structural complexity as a quantitative measure of visual complexity
topic	Physics and Society Computer Vision and Pattern Recognition Human-Computer Interaction
url	https://arxiv.org/abs/2408.04076

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