ML Anthology

On the Design of Robust Classifiers for Computer Vision

Hamed Masnadi-Shirazi, Vijay Mahadevan, Nuno Vasconcelos
CVPR 2010 pp. 779-786
doi:10.1109/CVPR.2010.5540136 /cvpr/2010/masnadishirazi2010cvpr-design/

Abstract

The design of robust classifiers, which can contend with the noisy and outlier ridden datasets typical of computer vision, is studied. It is argued that such robustness requires loss functions that penalize both large positive and negative margins. The probability elicitation view of classifier design is adopted, and a set of necessary conditions for the design of such losses is identified. These conditions are used to derive a novel robust Bayes-consistent loss, denoted Tangent loss, and an associated boosting algorithm, denoted TangentBoost. Experiments with data from the computer vision problems of scene classification, object tracking, and multiple instance learning show that TangentBoost consistently outperforms previous boosting algorithms.

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Text

Masnadi-Shirazi et al. "On the Design of Robust Classifiers for Computer Vision." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2010. doi:10.1109/CVPR.2010.5540136

Markdown

[Masnadi-Shirazi et al. "On the Design of Robust Classifiers for Computer Vision." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2010.](https://mlanthology.org/cvpr/2010/masnadishirazi2010cvpr-design/) doi:10.1109/CVPR.2010.5540136

BibTeX

@inproceedings{masnadishirazi2010cvpr-design,
  title     = {{On the Design of Robust Classifiers for Computer Vision}},
  author    = {Masnadi-Shirazi, Hamed and Mahadevan, Vijay and Vasconcelos, Nuno},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2010},
  pages     = {779-786},
  doi       = {10.1109/CVPR.2010.5540136},
  url       = {https://mlanthology.org/cvpr/2010/masnadishirazi2010cvpr-design/}
}