Boosting Chain Learning for Object Detection

A general classification framework, called boosting chain, is proposed for learning boosting cascade. In this framework, a "chain" structure is introduced to integrate historical knowledge into successive boosting learning. Moreover, a linear optimization scheme is proposed to address the problems of redundancy in boosting learning and threshold adjusting in cascade coupling. By this means, the resulting classifier consists of fewer weak classifiers yet achieves lower error rates than boosting cascade in both training and test. Experimental comparisons of boosting chain and boosting cascade are provided through a face detection problem. The promising results clearly demonstrate the effectiveness made by boosting chain.

• Type: Conference
• Year: 2003
• Where: ICCV
• Authors: Rong Xiao, Long Zhu, HongJiang Zhang
• Keywords: Cascade Coupling, Computer Vision, Fewer Weak Classifiers, General Classification Framework, ICCV 2003, Linear Optimization Scheme, Successive Boosting