Accelerating arrays of linear classifiers using approximate range queries

Victor Lu; Ian Endres; Matei Stroila; John C. Hart

doi:10.1109/WACV.2014.6836092

Accelerating arrays of linear classifiers using approximate range queries

Victor Lu, Ian Endres, Matei Stroila, John C. Hart

Computer Science

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

Abstract

Modern object detection methods apply binary linear classifiers on Euclidean feature vectors. This paper shows that projecting feature vectors onto a hypersphere allows an approximate range query to accelerate these detectors within acceptable levels of accuracy. The expense of constructing the k-d tree used by these range queries is justified when many detectors are used. We demonstrate our acceleration technique on several existing detection systems, including a state of the art logo detector, and show that approximate range queries can detect logos at least half as well at 11× the speed of the full fidelity method.

Original language	English (US)
Title of host publication	2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
Publisher	IEEE Computer Society
Pages	255-260
Number of pages	6
ISBN (Print)	9781479949854
DOIs	https://doi.org/10.1109/WACV.2014.6836092
State	Published - 2014
Event	2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014 - Steamboat Springs, CO, United States Duration: Mar 24 2014 → Mar 26 2014

Publication series

Name	2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014

Other

Other	2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
Country/Territory	United States
City	Steamboat Springs, CO
Period	3/24/14 → 3/26/14

ASJC Scopus subject areas

Computer Science Applications
Computer Vision and Pattern Recognition

Online availability

10.1109/WACV.2014.6836092

Library availability

Discover UIUC Full Text

Cite this

Lu, V., Endres, I., Stroila, M., & Hart, J. C. (2014). Accelerating arrays of linear classifiers using approximate range queries. In 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014 (pp. 255-260). [6836092] (2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014). IEEE Computer Society. https://doi.org/10.1109/WACV.2014.6836092

Accelerating arrays of linear classifiers using approximate range queries. / Lu, Victor; Endres, Ian; Stroila, Matei et al.
2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014. IEEE Computer Society, 2014. p. 255-260 6836092 (2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014).

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

Lu, V, Endres, I, Stroila, M & Hart, JC 2014, Accelerating arrays of linear classifiers using approximate range queries. in 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014., 6836092, 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014, IEEE Computer Society, pp. 255-260, 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014, Steamboat Springs, CO, United States, 3/24/14. https://doi.org/10.1109/WACV.2014.6836092

@inproceedings{73fa1ffc25ee48939ef8aab4b15c3545,

title = "Accelerating arrays of linear classifiers using approximate range queries",

abstract = "Modern object detection methods apply binary linear classifiers on Euclidean feature vectors. This paper shows that projecting feature vectors onto a hypersphere allows an approximate range query to accelerate these detectors within acceptable levels of accuracy. The expense of constructing the k-d tree used by these range queries is justified when many detectors are used. We demonstrate our acceleration technique on several existing detection systems, including a state of the art logo detector, and show that approximate range queries can detect logos at least half as well at 11× the speed of the full fidelity method.",

author = "Victor Lu and Ian Endres and Matei Stroila and Hart, {John C.}",

year = "2014",

doi = "10.1109/WACV.2014.6836092",

language = "English (US)",

isbn = "9781479949854",

series = "2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014",

publisher = "IEEE Computer Society",

pages = "255--260",

booktitle = "2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014",

note = "2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014 ; Conference date: 24-03-2014 Through 26-03-2014",

}

TY - GEN

T1 - Accelerating arrays of linear classifiers using approximate range queries

AU - Lu, Victor

AU - Endres, Ian

AU - Stroila, Matei

AU - Hart, John C.

PY - 2014

Y1 - 2014

N2 - Modern object detection methods apply binary linear classifiers on Euclidean feature vectors. This paper shows that projecting feature vectors onto a hypersphere allows an approximate range query to accelerate these detectors within acceptable levels of accuracy. The expense of constructing the k-d tree used by these range queries is justified when many detectors are used. We demonstrate our acceleration technique on several existing detection systems, including a state of the art logo detector, and show that approximate range queries can detect logos at least half as well at 11× the speed of the full fidelity method.

AB - Modern object detection methods apply binary linear classifiers on Euclidean feature vectors. This paper shows that projecting feature vectors onto a hypersphere allows an approximate range query to accelerate these detectors within acceptable levels of accuracy. The expense of constructing the k-d tree used by these range queries is justified when many detectors are used. We demonstrate our acceleration technique on several existing detection systems, including a state of the art logo detector, and show that approximate range queries can detect logos at least half as well at 11× the speed of the full fidelity method.

UR - http://www.scopus.com/inward/record.url?scp=84904615088&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84904615088&partnerID=8YFLogxK

U2 - 10.1109/WACV.2014.6836092

DO - 10.1109/WACV.2014.6836092

M3 - Conference contribution

AN - SCOPUS:84904615088

SN - 9781479949854

T3 - 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014

SP - 255

EP - 260

BT - 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014

PB - IEEE Computer Society

T2 - 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014

Y2 - 24 March 2014 through 26 March 2014

ER -

Accelerating arrays of linear classifiers using approximate range queries

Abstract

Publication series

Other

ASJC Scopus subject areas

Online availability

Library availability

Related links

Fingerprint

Cite this