Two-Class Weather Classification

Cewu Lu, Di Lin, Jiaya Jia, Chi-Keung Tang

CVPR 2014

doi:10.1109/CVPR.2014.475 /cvpr/2014/lu2014cvpr-twoclass/

Abstract

Given a single outdoor image, this paper proposes a collaborative learning approach for labeling it as either sunny or cloudy. Never adequately addressed, this twoclass classification problem is by no means trivial given the great variety of outdoor images. Our weather feature combines special cues after properly encoding them into feature vectors. They then work collaboratively in synergy under a unified optimization framework that is aware of the presence (or absence) of a given weather cue during learning and classification. Extensive experiments and comparisons are performed to verify our method. We build a new weather image dataset consisting of 10K sunny and cloudy images, which is available online together with the executable.

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Cite

Text

Lu et al. "Two-Class Weather Classification." Conference on Computer Vision and Pattern Recognition, 2014. doi:10.1109/CVPR.2014.475

Markdown

[Lu et al. "Two-Class Weather Classification." Conference on Computer Vision and Pattern Recognition, 2014.](https://mlanthology.org/cvpr/2014/lu2014cvpr-twoclass/) doi:10.1109/CVPR.2014.475

BibTeX

@inproceedings{lu2014cvpr-twoclass,
  title     = {{Two-Class Weather Classification}},
  author    = {Lu, Cewu and Lin, Di and Jia, Jiaya and Tang, Chi-Keung},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2014},
  doi       = {10.1109/CVPR.2014.475},
  url       = {https://mlanthology.org/cvpr/2014/lu2014cvpr-twoclass/}
}