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.
Original language | English |
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Pages (from-to) | 11-18 |
Number of pages | 8 |
Journal | Pattern Recognition Letters |
Volume | 40 |
Issue number | 1 |
DOIs | |
Publication status | Published - 15 Apr 2014 |
Keywords
- Boosting
- Cascade
- Information recycling
- Object detection
ASJC Scopus subject areas
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence