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Gated Classifiers: Boosting under high intra-class variation
KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
2011 (engelsk)Inngår i: Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, 2011, s. 2673-2680Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

In this paper we address the problem of using boosting (e.g. AdaBoost [7]) to classify a target class with significant intra-class variation against a large background class. This situation occurs for example when we want to recognize a visual object class against all other image patches. The boosting algorithm produces a strong classifier, which is a linear combination of weak classifiers. We observe that we often have sets of weak classifiers that individually fire on many examples of the target class but never fire together on those examples (i.e. their outputs are anti-correlated on the target class). Motivated by this observation we suggest a family of derived weak classifiers, termed gated classifiers, that suppress such combinations of weak classifiers. Gated classifiers can be used on top of any original weak learner. We run experiments on two popular datasets, showing that our method reduces the required number of weak classifiers by almost an order of magnitude, which in turn yields faster detectors. We experiment on synthetic data showing that gated classifiers enables more complex distributions to be represented. We hope that gated classifiers will extend the usefulness of boosted classifier cascades [29].

sted, utgiver, år, opplag, sider
2011. s. 2673-2680
Serie
IEEE Conference on Computer Vision and Pattern Recognition, ISSN 1063-6919
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-38595DOI: 10.1109/CVPR.2011.5995408ISI: 000295615802108Scopus ID: 2-s2.0-80052879417ISBN: 978-1-4577-0393-5 (tryckt)OAI: oai:DiVA.org:kth-38595DiVA, id: diva2:437455
Konferanse
The 24th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011, Colorado Springs, CO, USA, 20-25 June 2011 2011
Forskningsfinansiär
ICT - The Next Generation
Merknad
QC 20110830Tilgjengelig fra: 2011-08-29 Laget: 2011-08-29 Sist oppdatert: 2018-01-12bibliografisk kontrollert

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