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The Design of High-Level Features for Photo Quality Assessment
Abstract
We propose a principled method for designing high level features forphoto quality assessment. Our resulting system can classify between high quality professional photos and low quality snapshots. Instead of using the bag of low-level features approach, we first determine the perceptual factors that distinguish between professional photos and snapshots. Then, we design high level semantic features to measure the perceptual differences. We test our features on a large and diverse dataset and our system is able to achieve a classification rate of 72% on this difficult task. Since our system is able to achieve a precision of over 90% in low recall scenarios, we show excellent results in a web image search application.
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Cited by
AVA: A large-scale database for aesthetic visual analysis
Murray, N., Marchesotti, L., & Perronnin, F. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247954 · Cited by 936
Photo and Video Quality Evaluation: Focusing on the Subject
Luo, Y., & Tang, X. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88690-7_29 · Cited by 444
Self-citationA Deep Network Solution for Attention and Aesthetics Aware Photo Cropping
Wang, W., Shen, J., & Ling, H. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2840724 · Cited by 348
Image Aesthetic Assessment: An experimental survey
Deng, Y., Loy, C. C., & Tang, X. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2696576 · Cited by 306
“[9] are among the first to cast aesthetic understanding of images into a binary classification problem.”
BackgroundMethodInfluentialSelf-citationMa, S., Wei, Z., Tian, F., Fan, X., Zhang, J., Shen, X., Lin, Z., Huang, J., Měch, R., Samaras, D., et al. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300701 · Cited by 24
Metadata
- Authors
- Ke, Y., Tang, X., & Jing, F.
- Venue
- 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06)
- Publisher
- IEEE
- Published
- 2006-07-10
- Volume
- 1
- Pages
- 419-426
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2170658603
- Cited by
- 630
- References
- 0
- FWCI
- 8.77
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Ke, Y., Tang, X., & Jing, F. (2006). The Design of High-Level Features for Photo Quality Assessment. 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06). https://doi.org/10.1109/cvpr.2006.303
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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