Abstract
Image aesthetic assessment is important for finding well taken and appealing photographs but is challenging due to the ambiguity and subjectivity of aesthetic criteria. We develop the pairwise aesthetic comparison network (PAC-Net), which consists of two parts: aesthetic feature extraction and pairwise feature comparison. To alleviate the ambiguity and subjectivity, we train PAC-Net to learn the relative aesthetic ranks of two images by employing a novel loss function, called aesthetic-adaptive cross entropy loss. Then, we develop simple schemes for using PAC-Net in the tasks of aesthetic ranking and aesthetic classification, respectively. Experimental results demonstrate that PAC-Net achieves the state-of-the-art performances in both the ranking and classification applications.
| Original language | English |
|---|---|
| Title of host publication | 2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings |
| Publisher | IEEE Computer Society |
| Pages | 2491-2495 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781479970612 |
| DOIs | |
| Publication status | Published - 2018 Aug 29 |
| Event | 25th IEEE International Conference on Image Processing, ICIP 2018 - Athens, Greece Duration: 2018 Oct 7 → 2018 Oct 10 |
Publication series
| Name | Proceedings - International Conference on Image Processing, ICIP |
|---|---|
| ISSN (Print) | 1522-4880 |
Conference
| Conference | 25th IEEE International Conference on Image Processing, ICIP 2018 |
|---|---|
| Country | Greece |
| City | Athens |
| Period | 18/10/7 → 18/10/10 |
Fingerprint
Keywords
- Aesthetic ranking
- And pairwise comparison
- Convolutional neural networks
- Image aesthetic assessment
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition
- Signal Processing
Cite this
PAC-Net : Pairwise aesthetic comparison network for image aesthetic assessment. / Ko, Keunsoo; Lee, Jun Tae; Kim, Chang-Su.
2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings. IEEE Computer Society, 2018. p. 2491-2495 8451621 (Proceedings - International Conference on Image Processing, ICIP).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - PAC-Net
T2 - Pairwise aesthetic comparison network for image aesthetic assessment
AU - Ko, Keunsoo
AU - Lee, Jun Tae
AU - Kim, Chang-Su
PY - 2018/8/29
Y1 - 2018/8/29
N2 - Image aesthetic assessment is important for finding well taken and appealing photographs but is challenging due to the ambiguity and subjectivity of aesthetic criteria. We develop the pairwise aesthetic comparison network (PAC-Net), which consists of two parts: aesthetic feature extraction and pairwise feature comparison. To alleviate the ambiguity and subjectivity, we train PAC-Net to learn the relative aesthetic ranks of two images by employing a novel loss function, called aesthetic-adaptive cross entropy loss. Then, we develop simple schemes for using PAC-Net in the tasks of aesthetic ranking and aesthetic classification, respectively. Experimental results demonstrate that PAC-Net achieves the state-of-the-art performances in both the ranking and classification applications.
AB - Image aesthetic assessment is important for finding well taken and appealing photographs but is challenging due to the ambiguity and subjectivity of aesthetic criteria. We develop the pairwise aesthetic comparison network (PAC-Net), which consists of two parts: aesthetic feature extraction and pairwise feature comparison. To alleviate the ambiguity and subjectivity, we train PAC-Net to learn the relative aesthetic ranks of two images by employing a novel loss function, called aesthetic-adaptive cross entropy loss. Then, we develop simple schemes for using PAC-Net in the tasks of aesthetic ranking and aesthetic classification, respectively. Experimental results demonstrate that PAC-Net achieves the state-of-the-art performances in both the ranking and classification applications.
KW - Aesthetic ranking
KW - And pairwise comparison
KW - Convolutional neural networks
KW - Image aesthetic assessment
UR - http://www.scopus.com/inward/record.url?scp=85062908586&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062908586&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2018.8451621
DO - 10.1109/ICIP.2018.8451621
M3 - Conference contribution
AN - SCOPUS:85062908586
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 2491
EP - 2495
BT - 2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
PB - IEEE Computer Society
ER -