Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/17246
Title: A biased selection strategy for information recycling in boosting cascade visual-object detectors
Authors: Sun, C
Hu, J
Lam, KM 
Keywords: Boosting
Cascade
Information recycling
Object detection
Issue Date: 2014
Publisher: Elsevier Science Bv
Source: Pattern recognition letters, 2014, v. 40, no. 1, p. 11-18 How to cite?
Journal: Pattern Recognition Letters 
Abstract: We study the problem of information recycling in Boosting cascade visual-object detectors. It is believed that information obtained in the earlier stages of the cascade detector is also beneficial for the later stages, and that a more efficient detector can be constructed by recycling the existing information. In this work, we propose a biased selection strategy that promotes re-using existing information when selecting weak classifiers or features in each Boosting iteration. The strategy used can be interpreted as introducing a cardinality-based cost term to the Boosting loss function, and we solve the learning problem in a step-wise manner, similar to the gradient-Boosting scheme. Our work provides an alternative to the popular sparsity-inducing norms in solving such problems. Experimental results show that our method is superior to the existing methods.
URI: http://hdl.handle.net/10397/17246
ISSN: 0167-8655
DOI: 10.1016/j.patrec.2013.12.006
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