ML Anthology

Generalized Projection Based M-Estimator: Theory and Applications

Sushil Mittal, Saket Anand, Peter Meer
CVPR 2011 pp. 2689-2696
doi:10.1109/CVPR.2011.5995514 /cvpr/2011/mittal2011cvpr-generalized/

Abstract

We introduce a robust estimator called generalized projection based M-estimator (gpbM) which does not require the user to specify any scale parameters. For multiple inlier structures, with different noise covariances, the estimator iteratively determines one inlier structure at a time. Unlike pbM, where the scale of the inlier noise is estimated simultaneously with the model parameters, gpbM has three distinct stages-scale estimation, robust model estimation and inlier/outlier dichotomy. We evaluate our performance on challenging synthetic data, face image clustering upto ten different faces from Yale Face Database B and multi-body projective motion segmentation problem on Hopkins155 dataset. Results of state-of-the-art methods are presented for comparison.

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Cite

Text

Mittal et al. "Generalized Projection Based M-Estimator: Theory and Applications." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2011. doi:10.1109/CVPR.2011.5995514

Markdown

[Mittal et al. "Generalized Projection Based M-Estimator: Theory and Applications." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2011.](https://mlanthology.org/cvpr/2011/mittal2011cvpr-generalized/) doi:10.1109/CVPR.2011.5995514

BibTeX

@inproceedings{mittal2011cvpr-generalized,
  title     = {{Generalized Projection Based M-Estimator: Theory and Applications}},
  author    = {Mittal, Sushil and Anand, Saket and Meer, Peter},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2011},
  pages     = {2689-2696},
  doi       = {10.1109/CVPR.2011.5995514},
  url       = {https://mlanthology.org/cvpr/2011/mittal2011cvpr-generalized/}
}