ML Anthology

Efficient Region Search for Object Detection

Sudheendra Vijayanarasimhan, Kristen Grauman
CVPR 2011 pp. 1401-1408
doi:10.1109/CVPR.2011.5995545 /cvpr/2011/vijayanarasimhan2011cvpr-efficient/

Abstract

We propose a branch-and-cut strategy for efficient region-based object detection. Given an oversegmented image, our method determines the subset of spatially contiguous regions whose collective features will maximize a classifier's score. We formulate the objective as an instance of the prize-collecting Steiner tree problem, and show that for a family of additive classifiers this enables fast search for the optimal object region via a branch-and-cut algorithm. Unlike existing branch-and-bounddetection methods designed for bounding boxes, our approach allows scoring of irregular shapes - which is especially critical for objects that do not conform to a rectangular window. We provide results on three challenging object detection datasets, and demonstrate the advantage of rapidly seeking best-scoring regions rather than subwindow rectangles.

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Cite

Text

Vijayanarasimhan and Grauman. "Efficient Region Search for Object Detection." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2011. doi:10.1109/CVPR.2011.5995545

Markdown

[Vijayanarasimhan and Grauman. "Efficient Region Search for Object Detection." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2011.](https://mlanthology.org/cvpr/2011/vijayanarasimhan2011cvpr-efficient/) doi:10.1109/CVPR.2011.5995545

BibTeX

@inproceedings{vijayanarasimhan2011cvpr-efficient,
  title     = {{Efficient Region Search for Object Detection}},
  author    = {Vijayanarasimhan, Sudheendra and Grauman, Kristen},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2011},
  pages     = {1401-1408},
  doi       = {10.1109/CVPR.2011.5995545},
  url       = {https://mlanthology.org/cvpr/2011/vijayanarasimhan2011cvpr-efficient/}
}