Accelerating arrays of linear classifiers using approximate range queries

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

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish (US)
Title of host publication2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
PublisherIEEE Computer Society
Pages255-260
Number of pages6
ISBN (Print)9781479949854
DOIs
StatePublished - Jan 1 2014
Event2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014 - Steamboat Springs, CO, United States
Duration: Mar 24 2014Mar 26 2014

Publication series

Name2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014

Other

Other2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
CountryUnited States
CitySteamboat Springs, CO
Period3/24/143/26/14

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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  • 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