Real-Time Coarse-to-Fine Topologically Preserving Segmentation

Jian Yao, Marko Boben, Sanja Fidler, Raquel Urtasun

CVPR 2015

doi:10.1109/CVPR.2015.7298913 /cvpr/2015/yao2015cvpr-realtime/

Abstract

In this paper, we tackle the problem of unsupervised segmentation in the form of superpixels. Our main emphasis is on speed and accuracy. We build on [31] to define the problem as a boundary and topology preserving Markov random field. We propose a coarse to fine optimization technique that speeds up inference in terms of the number of updates by an order of magnitude. Our approach is shown to outperform [31] while employing a single iteration. We evaluate and compare our approach to state-of-the-art superpixel algorithms on the BSD and KITTI benchmarks. Our approach significantly outperforms the baselines in the segmentation metrics and achieves the lowest error on the stereo task.

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Cite

Text

Yao et al. "Real-Time Coarse-to-Fine Topologically Preserving Segmentation." Conference on Computer Vision and Pattern Recognition, 2015. doi:10.1109/CVPR.2015.7298913

Markdown

[Yao et al. "Real-Time Coarse-to-Fine Topologically Preserving Segmentation." Conference on Computer Vision and Pattern Recognition, 2015.](https://mlanthology.org/cvpr/2015/yao2015cvpr-realtime/) doi:10.1109/CVPR.2015.7298913

BibTeX

@inproceedings{yao2015cvpr-realtime,
  title     = {{Real-Time Coarse-to-Fine Topologically Preserving Segmentation}},
  author    = {Yao, Jian and Boben, Marko and Fidler, Sanja and Urtasun, Raquel},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2015},
  doi       = {10.1109/CVPR.2015.7298913},
  url       = {https://mlanthology.org/cvpr/2015/yao2015cvpr-realtime/}
}