Efficiently Learning a Detection Cascade with Sparse Eigenvectors

Details Efficiently Learning a Detection Cascade with Sparse Eigenvectors Efficiently Learning a Detection Cascade with Sparse Eigenvectors

2009-03-18

arxiv.org/abs/0903.3103v1

Computer Science

Multimedia

12 pages, conference version published in CVPR2009

Scientific paper

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.

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