COB – Convolutional Oriented Boundaries

Publications

K.K. Maninis, J. Pont-Tuset, P. Arbeláez, and L. Van Gool
Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), vol. 40, no. 4, pp. 819 - 833, 2018.
[PDF] [BibTex]

@article{Man+18a,
  author = {Kevis-Kokitsi Maninis and Jordi Pont-Tuset and Pablo Arbel\'{a}ez and Luc Van Gool},
  title = {Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
  year = {2018},
  volume = {40},
  number = {4},
  pages = {819 - 833}
}

K.K. Maninis, J. Pont-Tuset, P. Arbeláez, and L. Van Gool
Convolutional Oriented Boundaries
European Conference on Computer Vision (ECCV) 2016
[PDF] [BibTex]

@inproceedings{Man+16a,
  author = {Kevis-Kokitsi Maninis and Jordi Pont-Tuset and Pablo Arbel\'{a}ez and Luc Van Gool},
  title = {Convolutional Oriented Boundaries},
  booktitle = {European Conference on Computer Vision (ECCV)},
  year = {2016}
}

Please cite these papers if you found the resources of this web useful.

Abstract

This paper presents Convolutional Oriented Boundaries (COB), which produces multiscale oriented contours and region hierarchies starting from generic image classification CNNs. COB is computationally efficient, because it requires a single CNN forward pass for contour detection and uses a novel sparse boundary representation for hierarchical segmentation; it gives a significant leap in performance over the state-of-the-art, and it generalizes very well to unseen categories and datasets. We perform extensive experiments on BSDS, PASCAL Context, PASCAL Segmentation, and MS-COCO, showing that COB provides state-of-the-art contours, region hierarchies, and object proposals.