Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/74723
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dc.contributor.authorShen, C.en
dc.contributor.authorWang, P.en
dc.contributor.authorPaisitkriangkrai, S.en
dc.contributor.authorVan Den Hengel, A.en
dc.date.issued2013en
dc.identifier.citationInternational Journal of Computer Vision, 2013; 103(3):326-347en
dc.identifier.issn0920-5691en
dc.identifier.issn1573-1405en
dc.identifier.urihttp://hdl.handle.net/2440/74723-
dc.descriptionExtent: 23p. The final publication is available at www.springerlink.com: http://link.springer.com/article/10.1007/s11263-013-0608-1en
dc.description.abstractCascade 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.en
dc.description.statementofresponsibilityChunhua Shen, Peng Wang, Sakrapee Paisitkriangkrai, Anton van den Hengelen
dc.language.isoenen
dc.publisherKluwer Academic Publen
dc.rights© Springer Science+Business Media New York 2013en
dc.subjectAdaBoost; Minimax Probability Machine; Cascade Classifier; Object Detection; Human Detectionen
dc.titleTraining effective node classifiers for cascade classificationen
dc.typeJournal articleen
dc.identifier.rmid0020124826en
dc.identifier.doi10.1007/s11263-013-0608-1en
dc.relation.granthttp://purl.org/au-research/grants/arc/FT120100969en
dc.identifier.pubid21588-
pubs.library.collectionComputer Science publicationsen
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidShen, C. [0000-0002-8648-8718]en
dc.identifier.orcidVan Den Hengel, A. [0000-0003-3027-8364]en
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