Training Effective Node Classifiers for Cascade Classification

TL;DR

This paper introduces a new boosting algorithm tailored for cascade classifiers in object detection, explicitly optimizing for high detection rates and moderate false positives, resulting in improved detection performance.

Contribution

It presents a principled feature selection method based on a biased minimax probability machine, linking it to the linear asymmetric classifier, and develops a totally-corrective boosting algorithm for cascade node classification.

Findings

The proposed boosting algorithm outperforms current state-of-the-art methods.

Experimental results confirm improved detection accuracy in object detection tasks.

The method effectively balances detection rate and false positive rate in cascade classifiers.

Abstract

Cascade classifiers are widely used in real-time object detection. Different from conventional classifiers that are designed for a low overall classification error rate, a classifier in each node of the cascade is required to achieve an extremely high detection rate and moderate false positive rate. Although there are a few reported methods addressing this requirement in the context of object detection, there is no principled feature selection method that explicitly takes into account this asymmetric node learning objective. We provide such an algorithm here. We show that a special case of the biased minimax probability machine has the same formulation as the linear asymmetric classifier (LAC) of Wu et al (2005). We then design a new boosting algorithm that directly optimizes the cost function of LAC. The resulting totally-corrective boosting algorithm is implemented by the column generation technique in convex optimization. Experimental results on object detection verify the effectiveness of the proposed boosting algorithm as a node classifier in cascade object detection, and show performance better than that of the current state-of-the-art.