ML Anthology

Semi-Supervised Learning Based Object Detection in Aerial Imagery

Jian Yao, Zhongfei (Mark) Zhang
CVPR 2005 pp. 1011-1016
doi:10.1109/CVPR.2005.318 /cvpr/2005/yao2005cvpr-semi/

Abstract

Object detection in aerial imagery has been well studied in computer vision for years. However, given the complexity of large variations of the appearance of the object and the background in a typical aerial image, a robust and efficient detection is still considered as an open and challenging problem. In this paper, we have developed a theoretic foundation for aerial imagery object detection using semi-supervised learning algorithms. Based on this theory, we have proposed a context-based object detection methodology. Both theoretic analyses and experimental evaluations have successfully demonstrated the great promise of the developed theory and the related detection methodology.

CVPR Semantic Scholar

Cite

Text

Yao and Zhang. "Semi-Supervised Learning Based Object Detection in Aerial Imagery." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005. doi:10.1109/CVPR.2005.318

Markdown

[Yao and Zhang. "Semi-Supervised Learning Based Object Detection in Aerial Imagery." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005.](https://mlanthology.org/cvpr/2005/yao2005cvpr-semi/) doi:10.1109/CVPR.2005.318

BibTeX

@inproceedings{yao2005cvpr-semi,
  title     = {{Semi-Supervised Learning Based Object Detection in Aerial Imagery}},
  author    = {Yao, Jian and Zhang, Zhongfei (Mark)},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2005},
  pages     = {1011-1016},
  doi       = {10.1109/CVPR.2005.318},
  url       = {https://mlanthology.org/cvpr/2005/yao2005cvpr-semi/}
}