Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/74723
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Type: Journal article
Title: Training effective node classifiers for cascade classification
Author: Shen, C.
Wang, P.
Paisitkriangkrai, S.
Van Den Hengel, A.
Citation: International Journal of Computer Vision, 2013; 103(3):326-347
Publisher: Kluwer Academic Publ
Issue Date: 2013
ISSN: 0920-5691
1573-1405
Statement of
Responsibility: 
Chunhua Shen, Peng Wang, Sakrapee Paisitkriangkrai, Anton van den Hengel
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.
Keywords: AdaBoost; Minimax Probability Machine; Cascade Classifier; Object Detection; Human Detection
Description: Extent: 23p. The final publication is available at www.springerlink.com: http://link.springer.com/article/10.1007/s11263-013-0608-1
Rights: © Springer Science+Business Media New York 2013
RMID: 0020124826
DOI: 10.1007/s11263-013-0608-1
Grant ID: http://purl.org/au-research/grants/arc/FT120100969
Appears in Collections:Computer Science publications

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