Efficiently Learning a Detection Cascade with Sparse Eigenvectors
Multimedia
2009-03-19 v1 Artificial Intelligence
Machine Learning
Abstract
In this work, we first show that feature selection methods other than boosting can also be used for training an efficient object detector. In particular, we introduce Greedy Sparse Linear Discriminant Analysis (GSLDA) \cite{Moghaddam2007Fast} for its conceptual simplicity and computational efficiency; and slightly better detection performance is achieved compared with \cite{Viola2004Robust}. Moreover, we propose a new technique, termed Boosted Greedy Sparse Linear Discriminant Analysis (BGSLDA), to efficiently train a detection cascade. BGSLDA exploits the sample re-weighting property of boosting and the class-separability criterion of GSLDA.
Keywords
Cite
@article{arxiv.0903.3103,
title = {Efficiently Learning a Detection Cascade with Sparse Eigenvectors},
author = {Chunhua Shen and Sakrapee Paisitkriangkrai and Jian Zhang},
journal= {arXiv preprint arXiv:0903.3103},
year = {2009}
}
Comments
12 pages, conference version published in CVPR2009