IAACS: Image Aesthetic Assessment Through Color Composition And Space Formation

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

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