کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
535430 | 870346 | 2014 | 8 صفحه PDF | دانلود رایگان |
• Information recycling for boosting cascade detectors is systematically studied.
• A biased selection strategy for weak classifier recycling is proposed.
• The strategy is extended for recycling features and fully exploiting their power.
• A more efficient visual-object detector is trained using the proposed technique.
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.
Journal: Pattern Recognition Letters - Volume 40, 15 April 2014, Pages 11–18