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.
Cite
Text
Lu et al. "Accelerating Arrays of Linear Classifiers Using Approximate Range Queries." IEEE/CVF Winter Conference on Applications of Computer Vision, 2014. doi:10.1109/WACV.2014.6836092Markdown
[Lu et al. "Accelerating Arrays of Linear Classifiers Using Approximate Range Queries." IEEE/CVF Winter Conference on Applications of Computer Vision, 2014.](https://mlanthology.org/wacv/2014/lu2014wacv-accelerating/) doi:10.1109/WACV.2014.6836092BibTeX
@inproceedings{lu2014wacv-accelerating,
title = {{Accelerating Arrays of Linear Classifiers Using Approximate Range Queries}},
author = {Lu, Victor and Endres, Ian and Stroila, Matei and Hart, John C.},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2014},
pages = {255-260},
doi = {10.1109/WACV.2014.6836092},
url = {https://mlanthology.org/wacv/2014/lu2014wacv-accelerating/}
}