Complexity-Adaptive Distance Metric for Object Proposals Generation

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

CVPR 2015poster47 citations

Complexity-Adaptive Distance Metric for Object Proposals Generation

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

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.

BibTeX

@inproceedings{cvpr2015_complexityadapti,
  title = {Complexity-Adaptive Distance Metric for Object Proposals Generation},
  author = {Yao Xiao and Cewu Lu and Efstratios Tsougenis and Yongyi Lu and Chi-Keung Tang},
  booktitle = {CVPR 2015},
  year = {2015}
}