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The Design of High-Level Features for Photo Quality Assessment

Yan Ke, Xiaoou Tang, Feng Jing
CVPR 2006 pp. 419-426
doi:10.1109/CVPR.2006.303 /cvpr/2006/ke2006cvpr-design/

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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Cite

Text

Ke et al. "The Design of High-Level Features for Photo Quality Assessment." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2006. doi:10.1109/CVPR.2006.303

Markdown

[Ke et al. "The Design of High-Level Features for Photo Quality Assessment." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2006.](https://mlanthology.org/cvpr/2006/ke2006cvpr-design/) doi:10.1109/CVPR.2006.303

BibTeX

@inproceedings{ke2006cvpr-design,
  title     = {{The Design of High-Level Features for Photo Quality Assessment}},
  author    = {Ke, Yan and Tang, Xiaoou and Jing, Feng},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2006},
  pages     = {419-426},
  doi       = {10.1109/CVPR.2006.303},
  url       = {https://mlanthology.org/cvpr/2006/ke2006cvpr-design/}
}