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Efficiently training a better visual detector with sparse eigenvectors

10 years 19 hour ago
Efficiently training a better visual detector with sparse eigenvectors
Face detection plays an important role in many vision applications. Since Viola and Jones [1] proposed the first real-time AdaBoost based object detection system, much effort has been spent on improving the boosting method. 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 have adopted Greedy Sparse Linear Discriminant Analysis (GSLDA) [2] for its computational efficiency; and slightly better detection performance is achieved compared with [1]. Moreover, we propose a new technique, termed Boosted Greedy Sparse Linear Discriminant Analysis (BGSLDA), to efficiently train object detectors. BGSLDA exploits the sample re-weighting property of boosting and the class-separability criterion of GSLDA. Experiments in the domain of highly skewed data distributions, e.g., face detection, demonstrates that classifiers trained with the proposed BGSLDA outperforms AdaBoost and its variants. Thi...
Sakrapee Paisitkriangkrai, Chunhua Shen, Jian Zhan
Added 16 Aug 2010
Updated 16 Aug 2010
Type Conference
Year 2009
Where CVPR
Authors Sakrapee Paisitkriangkrai, Chunhua Shen, Jian Zhang
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