Oriented Edge Forests for Boundary Detection

CVPR 2015

doi:10.1109/CVPR.2015.7298782 /cvpr/2015/hallman2015cvpr-oriented/

Abstract

We present a simple, efficient model for learning boundary detection based on a random forest classifier. Our approach combines (1) efficient clustering of training examples based on a simple partitioning of the space of local edge orientations and (2) scale-dependent calibration of individual tree output probabilities prior to multiscale combination. The resulting model outperforms published results on the challenging BSDS500 boundary detection benchmark. Further, on large datasets our model requires substantially less memory for training and speeds up training time by a factor of 10 over the structured forest model.

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Cite

Text

Hallman and Fowlkes. "Oriented Edge Forests for Boundary Detection." Conference on Computer Vision and Pattern Recognition, 2015. doi:10.1109/CVPR.2015.7298782

Markdown

[Hallman and Fowlkes. "Oriented Edge Forests for Boundary Detection." Conference on Computer Vision and Pattern Recognition, 2015.](https://mlanthology.org/cvpr/2015/hallman2015cvpr-oriented/) doi:10.1109/CVPR.2015.7298782

BibTeX

@inproceedings{hallman2015cvpr-oriented,
  title     = {{Oriented Edge Forests for Boundary Detection}},
  author    = {Hallman, Sam and Fowlkes, Charless C.},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2015},
  doi       = {10.1109/CVPR.2015.7298782},
  url       = {https://mlanthology.org/cvpr/2015/hallman2015cvpr-oriented/}
}