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arXiv · 1005.4103

LACBoost and FisherBoost: Optimally Building Cascade Classifiers

Chunhua Shen·Peng Wang·Hanxi Li
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Abstract

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 objective. 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.

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Chunhua Shen, Peng Wang, Hanxi Li. 2010-05-22. LACBoost and FisherBoost: Optimally Building Cascade Classifiers. https://arxiv.org/abs/1005.4103

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