Complexity-Adaptive Distance Metric for Object Proposals Generation

Yao Xiao, Cewu Lu, Efstratios Tsougenis, Yongyi Lu, Chi-Keung Tang

CVPR 2015

doi:10.1109/CVPR.2015.7298678 /cvpr/2015/xiao2015cvpr-complexityadaptive/

Abstract

Distance metric plays a key role in grouping superpixels to produce object proposals for object detection. We observe that existing distance metrics work primarily for low complexity cases. In this paper, we develop a novel distance metric for grouping two superpixels in high-complexity scenarios. Combining them, a complexity-adaptive distance measure is produced that achieves improved grouping in different levels of complexity. Our extensive experimentation shows that our method can achieve good results in the PASCAL VOC 2012 dataset surpassing the latest state-of-the-art methods.

PDF CVPR Semantic Scholar

Cite

Text

Xiao et al. "Complexity-Adaptive Distance Metric for Object Proposals Generation." Conference on Computer Vision and Pattern Recognition, 2015. doi:10.1109/CVPR.2015.7298678

Markdown

[Xiao et al. "Complexity-Adaptive Distance Metric for Object Proposals Generation." Conference on Computer Vision and Pattern Recognition, 2015.](https://mlanthology.org/cvpr/2015/xiao2015cvpr-complexityadaptive/) doi:10.1109/CVPR.2015.7298678

BibTeX

@inproceedings{xiao2015cvpr-complexityadaptive,
  title     = {{Complexity-Adaptive Distance Metric for Object Proposals Generation}},
  author    = {Xiao, Yao and Lu, Cewu and Tsougenis, Efstratios and Lu, Yongyi and Tang, Chi-Keung},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2015},
  doi       = {10.1109/CVPR.2015.7298678},
  url       = {https://mlanthology.org/cvpr/2015/xiao2015cvpr-complexityadaptive/}
}