Skip to main content

Research Repository

Advanced Search

IAACS: Image Aesthetic Assessment Through Color Composition And Space Formation

Yang, Bailin; zhu, Changrui; Li, Frederick W.B.; Wei, Tianxiang; Liang, Xiaohui; Wang, Qingxu

IAACS: Image Aesthetic Assessment Through Color Composition And Space Formation Thumbnail


Authors

Bailin Yang

Changrui zhu

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






You might also like



Downloadable Citations