As the number of digital photos increases, the need for image aesthetic assessment is increasing in various applications to provide improved user satisfaction. Most existing studies have considered binary classification to determine whether an image has a high- or low-level of aesthetic quality. However, the binary classification has a limitation in that when an image is classified incorrectly, users experience a large gap between their perception and the predicted result. To reduce the gap, we propose ternary classification-based image aesthetic assessment. Through experiments using popular classification deep learning models, we show the advantages of the ternary classification over the binary classification.
|Title of host publication||2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 2020 Nov 1|
|Event||2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020 - Seoul, Korea, Republic of|
Duration: 2020 Nov 1 → 2020 Nov 3
|Name||2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020|
|Conference||2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020|
|Country/Territory||Korea, Republic of|
|Period||20/11/1 → 20/11/3|
Bibliographical noteFunding Information:
This work was supported Intelligence Graduate School University, 2020-0-01361).
This work was supported by the Artificial Intelligence Graduate School Program (Yonsei University, 2020-0-01361).
© 2020 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Electrical and Electronic Engineering
- Media Technology