Multiscale Combinatorial Grouping

Pablo Arbelaez, Jordi Pont-Tuset, Jonathan T. Barron, Ferran Marques, Jitendra Malik

CVPR 2014

doi:10.1109/CVPR.2014.49 /cvpr/2014/arbelaez2014cvpr-multiscale/

Abstract

We propose a unified approach for bottom-up hierarchical image segmentation and object candidate generation for recognition, called Multiscale Combinatorial Grouping (MCG). For this purpose, we first develop a fast normalized cuts algorithm. We then propose a high-performance hierarchical segmenter that makes effective use of multiscale information. Finally, we propose a grouping strategy that combines our multiscale regions into highly-accurate object candidates by exploring efficiently their combinatorial space. We conduct extensive experiments on both the BSDS500 and on the PASCAL 2012 segmentation datasets, showing that MCG produces state-of-the-art contours, hierarchical regions and object candidates.

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Cite

Text

Arbelaez et al. "Multiscale Combinatorial Grouping." Conference on Computer Vision and Pattern Recognition, 2014. doi:10.1109/CVPR.2014.49

Markdown

[Arbelaez et al. "Multiscale Combinatorial Grouping." Conference on Computer Vision and Pattern Recognition, 2014.](https://mlanthology.org/cvpr/2014/arbelaez2014cvpr-multiscale/) doi:10.1109/CVPR.2014.49

BibTeX

@inproceedings{arbelaez2014cvpr-multiscale,
  title     = {{Multiscale Combinatorial Grouping}},
  author    = {Arbelaez, Pablo and Pont-Tuset, Jordi and Barron, Jonathan T. and Marques, Ferran and Malik, Jitendra},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2014},
  doi       = {10.1109/CVPR.2014.49},
  url       = {https://mlanthology.org/cvpr/2014/arbelaez2014cvpr-multiscale/}
}