Bailin Yang
IAACS: Image Aesthetic Assessment Through Color Composition And Space Formation
Yang, Bailin; zhu, Changrui; Li, Frederick W.B.; Wei, Tianxiang; Liang, Xiaohui; Wang, Qingxu
Authors
Changrui zhu
Dr Frederick Li frederick.li@durham.ac.uk
Associate Professor
Tianxiang Wei
Xiaohui Liang
Qingxu Wang
Abstract
Judging how an image is visually appealing is a complicated and subjective task. This highly motivates having a machine learning model to automatically evaluate image aesthetic by matching the aesthetics of general public. Although deep learning methods have been successfully learning good visual features from images, correctly assessing image aesthetic quality is still challenging for deep learning. To tackle this, we propose a novel multi-view convolutional neural network to assess image aesthetic by analyzing image color composition and space formation (IAACS). Specifically, from different views of an image, including its key color components with their contributions, the image space formation and the image itself, our network extracts their corresponding features through our proposed feature extraction module (FET) and the ImageNet weight-based classification model. By fusing the extracted features, our network produces an accurate prediction score distribution of image aesthetic. Experiment results have shown that we have achieved a superior performance.
Citation
Yang, B., zhu, C., Li, F. W., Wei, T., Liang, X., & Wang, Q. (2023). IAACS: Image Aesthetic Assessment Through Color Composition And Space Formation. Virtual Reality & Intelligent Hardware, 5(1), https://doi.org/10.1016/j.vrih.2022.06.006
Journal Article Type | Article |
---|---|
Acceptance Date | Jun 23, 2022 |
Online Publication Date | Feb 28, 2023 |
Publication Date | 2023 |
Deposit Date | Jul 3, 2023 |
Publicly Available Date | Jul 3, 2023 |
Journal | Virtual Reality & Intelligent Hardware |
Print ISSN | 2096-5796 |
Electronic ISSN | 2096-5796 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 5 |
Issue | 1 |
DOI | https://doi.org/10.1016/j.vrih.2022.06.006 |
Public URL | https://durham-repository.worktribe.com/output/1168968 |
Files
Published Journal Article
(12 Mb)
PDF
Publisher Licence URL
http://creativecommons.org/licenses/by-nc-nd/4.0/
Copyright Statement
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by/4.0/).
You might also like
Advances in Web-Based Learning - ICWL 2015
(-0001)
Book
Tackling Data Bias in Painting Classification with Style Transfer
(2023)
Presentation / Conference Contribution
Aesthetic Enhancement via Color Area and Location Awareness
(2022)
Presentation / Conference Contribution
STIT: Spatio-Temporal Interaction Transformers for Human-Object Interaction Recognition in Videos
(2022)
Presentation / Conference Contribution
STGAE: Spatial-Temporal Graph Auto-Encoder for Hand Motion Denoising
(2021)
Presentation / Conference Contribution
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search