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International Journal of Computer Vision
Published in: International Journal of Computer Vision 3/2013

01-07-2013

Training Effective Node Classifiers for Cascade Classification

Authors: Chunhua Shen, Peng Wang, Sakrapee Paisitkriangkrai, Anton van den Hengel

Published in: International Journal of Computer Vision | Issue 3/2013

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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. (linear asymmetric classifier for cascade detectors, 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.
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Appendix
Available only for authorised users
Footnotes
1
In our object detection experiment, we found that this assumption can always be satisfied.
 
2
Since the multi-exit cascade makes use of all previous weak classifiers in earlier nodes, it would meet the Gaussianity requirement better than the conventional cascade classifier.
 
3
To train a complete \(22\)-node cascade and choose the best \( \theta \) on cross-validation data may give better detection rates.
 
4
Our implementation is in C++ and only the weak classifier training part is parallelized using OpenMP.
 
5
Covariance features capture the relationship between different image statistics and have been shown to perform well in our previous experiments. However, other discriminative features can also be used here instead, e.g., Haar-like features, local binary pattern (LBP) (Mu et al. 2008) and self-similarity of low-level features (CSS) (Walk et al. 2010).
 
6
Here \(({\varvec{\mu }}, {\varvec{\varSigma }})_\mathrm{S}\) denotes the family of distributions in \( ( {\varvec{\mu }}, {\varvec{\varSigma }}) \) that are also symmetric about the mean \( {\varvec{\mu }}.\) \(({\varvec{\mu }}, {\varvec{\varSigma }})_\mathrm{SU}\) denotes the family of distributions in \( ( {\varvec{\mu }}, {\varvec{\varSigma }}) \) that are additionally symmetric and linear unimodal about \( {\varvec{\mu }}.\)
 
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Metadata
Title
Training Effective Node Classifiers for Cascade Classification
Authors
Chunhua Shen
Peng Wang
Sakrapee Paisitkriangkrai
Anton van den Hengel
Publication date
01-07-2013
Publisher
Springer US
Published in
International Journal of Computer Vision / Issue 3/2013
Print ISSN: 0920-5691
Electronic ISSN: 1573-1405
DOI
https://doi.org/10.1007/s11263-013-0608-1

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