LACBoost and FisherBoost: Optimally Building Cascade Classifiers [article]

Chunhua Shen and Peng Wang and Hanxi Li
2010 arXiv   pre-print
Object detection is one of the key tasks in computer vision. The cascade framework of Viola and Jones has become the de facto standard. A classifier in each node of the cascade is required to achieve extremely high detection rates, instead of low overall classification error. Although there are a few reported methods addressing this requirement in the context of object detection, there is no a principled feature selection method that explicitly takes into account this asymmetric node learning
more » ... jective. We provide such a boosting algorithm in this work. It is inspired by the linear asymmetric classifier (LAC) of Wu et al. in that our boosting algorithm optimizes a similar cost function. The new totally-corrective boosting algorithm is implemented by the column generation technique in convex optimization. Experimental results on face detection suggest that our proposed boosting algorithms can improve the state-of-the-art methods in detection performance.
arXiv:1005.4103v1 fatcat:jaacgpwvlfa7bnzwc2q44m7cju