Semi-supervised learning based object detection in aerial imagery

Jian Yao; Zhongfei Zhang

doi:10.1109/CVPR.2005.318

Semi-supervised learning based object detection in aerial imagery

Jian Yao
, Zhongfei Zhang

School of Computing

Binghamton University

State University of New York Binghamton University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

19 Scopus citations

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.

Original language	English
Title of host publication	Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005
Publisher	IEEE Computer Society
Pages	1011-1016
Number of pages	6
ISBN (Print)	0769523722, 9780769523729
DOIs	https://doi.org/10.1109/CVPR.2005.318
State	Published - 2005
Event	2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 - San Diego, CA, United States Duration: Jun 20 2005 → Jun 25 2005

Publication series

Name	Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005
Volume	I

Conference

Conference	2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005
Country/Territory	United States
City	San Diego, CA
Period	06/20/05 → 06/25/05

Access to Document

10.1109/CVPR.2005.318

Cite this

Yao, J., & Zhang, Z. (2005). Semi-supervised learning based object detection in aerial imagery. In Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 (pp. 1011-1016). Article 1467377 (Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005; Vol. I). IEEE Computer Society. https://doi.org/10.1109/CVPR.2005.318

@inproceedings{93d833384318420299a699bd96915271,

title = "Semi-supervised learning based object detection in aerial imagery",

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.",

author = "Jian Yao and Zhongfei Zhang",

year = "2005",

doi = "10.1109/CVPR.2005.318",

language = "English",

isbn = "0769523722",

series = "Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005",

publisher = "IEEE Computer Society",

pages = "1011--1016",

booktitle = "Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005",

note = "2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 ; Conference date: 20-06-2005 Through 25-06-2005",

}

Yao, J & Zhang, Z 2005, Semi-supervised learning based object detection in aerial imagery. in Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005., 1467377, Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, IEEE Computer Society, pp. 1011-1016, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, San Diego, CA, United States, 06/20/05. https://doi.org/10.1109/CVPR.2005.318

Semi-supervised learning based object detection in aerial imagery. / Yao, Jian; Zhang, Zhongfei.
Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005. IEEE Computer Society, 2005. p. 1011-1016 1467377 (Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005; Vol. I).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Yao, Jian

AU - Zhang, Zhongfei

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N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/33745178490

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DO - 10.1109/CVPR.2005.318

M3 - Conference contribution

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SN - 9780769523729

T3 - Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005

SP - 1011

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BT - Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005

PB - IEEE Computer Society

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Y2 - 20 June 2005 through 25 June 2005

ER -

Yao J, Zhang Z. Semi-supervised learning based object detection in aerial imagery. In Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005. IEEE Computer Society. 2005. p. 1011-1016. 1467377. (Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005). doi: 10.1109/CVPR.2005.318

Semi-supervised learning based object detection in aerial imagery

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