Kyle-Davidson, Cameron, Bors, Adrian Gheorghe orcid.org/0000-0001-7838-0021 and Evans, Karla orcid.org/0000-0002-8440-1711 (2022) Predicting Human Perception of Scene Complexity. In: IEEE International Conference on Image Processing (ICIP). IEEE , Bordeaux, France , pp. 1281-1285.
Abstract
It is apparent that humans are intrinsically capable of determining the degree of complexity present in an image; but it is unclear which regions in that image lead humans towards evaluating an image as complex or simple. Here, we develop a novel deep learning model for predicting human perception of the complexity of natural scene images in order to address these problems. For a given image, our approach, ComplexityNet, can generate both single-score complexity ratings and two-dimensional per-pixel complexity maps. These complexity maps indicate the regions of scenes that humans find to be complex, or simple. Drawing on work in the cognitive sciences we integrate metrics for scene clutter and scene symmetry , and conclude that the proposed metrics do indeed boost neural network performance when predicting complexity.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © IEEE 2022. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) The University of York > Faculty of Sciences (York) > Psychology (York) |
Depositing User: | Pure (York) |
Date Deposited: | 09 Nov 2022 10:30 |
Last Modified: | 02 Apr 2025 23:34 |
Published Version: | https://doi.org/10.1109/ICIP46576.2022.9897953 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ICIP46576.2022.9897953 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:193132 |
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Description: Predicting Human Perception of Scene Complexity