Efficiently Learning a Detection Cascade with Sparse Eigenvectors
NICTA（澳大利亚国家信息与通信技术研究院）；Australian National University（澳大利亚国立大学）；University of New South Wales（新南威尔士大学）
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
