Efficiently Learning a Detection Cascade with Sparse Eigenvectors

Shen, Chunhua; Paisitkriangkrai, Sakrapee; Zhang, Jian

Efficiently Learning a Detection Cascade with Sparse Eigenvectors

Authors

Shen, Chunhua

Paisitkriangkrai, Sakrapee

Zhang, Jian

Description

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.
12 pages, conference version published in CVPR2009