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Article: Feature fusion at the local region using localized maximum-margin learning for scene categorization

TitleFeature fusion at the local region using localized maximum-margin learning for scene categorization
Authors
KeywordsFeature fusion
Image-based
Local feature
Local region
Multiple features
Issue Date2012
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/pr
Citation
Pattern Recognition, 2012, v. 45 n. 4, p. 1671-1683 How to Cite?
AbstractIn the field of visual recognition such as scene categorization, representing an image based on the local feature (e.g., the bag-of-visual-word (BOVW) model and the bag-of-contextual-visual-word (BOCVW) model) has become popular and one of the most successful methods. In this paper, we propose a method that uses localized maximum-margin learning to fuse different types of features during the BOCVW modeling for eventual scene classification. The proposed method fuses multiple features at the stage when the best contextual visual word is selected to represent a local region (hard assignment) or the probabilities of the candidate contextual visual words used to represent the unknown region are estimated (soft assignment). The merits of the proposed method are that (1) errors caused by the ambiguity of single feature when assigning local regions to the contextual visual words can be corrected or the probabilities of the candidate contextual visual words used to represent the region can be estimated more accurately; and that (2) it offers a more flexible way in fusing these features through determining the similarity-metric locally by localized maximum-margin learning. The proposed method has been evaluated experimentally and the results indicate its effectiveness. © 2011 Elsevier Ltd All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/152665
ISSN
2015 Impact Factor: 3.399
2015 SCImago Journal Rankings: 2.051
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorQin, Jen_US
dc.contributor.authorYung, NHCen_US
dc.date.accessioned2012-07-16T09:45:52Z-
dc.date.available2012-07-16T09:45:52Z-
dc.date.issued2012en_US
dc.identifier.citationPattern Recognition, 2012, v. 45 n. 4, p. 1671-1683en_US
dc.identifier.issn0031-3203-
dc.identifier.urihttp://hdl.handle.net/10722/152665-
dc.description.abstractIn the field of visual recognition such as scene categorization, representing an image based on the local feature (e.g., the bag-of-visual-word (BOVW) model and the bag-of-contextual-visual-word (BOCVW) model) has become popular and one of the most successful methods. In this paper, we propose a method that uses localized maximum-margin learning to fuse different types of features during the BOCVW modeling for eventual scene classification. The proposed method fuses multiple features at the stage when the best contextual visual word is selected to represent a local region (hard assignment) or the probabilities of the candidate contextual visual words used to represent the unknown region are estimated (soft assignment). The merits of the proposed method are that (1) errors caused by the ambiguity of single feature when assigning local regions to the contextual visual words can be corrected or the probabilities of the candidate contextual visual words used to represent the region can be estimated more accurately; and that (2) it offers a more flexible way in fusing these features through determining the similarity-metric locally by localized maximum-margin learning. The proposed method has been evaluated experimentally and the results indicate its effectiveness. © 2011 Elsevier Ltd All rights reserved.-
dc.languageengen_US
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/pren_US
dc.relation.ispartofPattern Recognitionen_US
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Pattern Recognition. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition, 2012, v. 45 n. 4, p. 1671-1683. DOI: 10.1016/j.patcog.2011.09.027-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectFeature fusion-
dc.subjectImage-based-
dc.subjectLocal feature-
dc.subjectLocal region-
dc.subjectMultiple features-
dc.titleFeature fusion at the local region using localized maximum-margin learning for scene categorizationen_US
dc.typeArticleen_US
dc.identifier.emailQin, J: jzhqin@eee.hku.hken_US
dc.identifier.emailYung, NHC: nyung@eee.hku.hk-
dc.identifier.authorityYung, NHC=rp00226en_US
dc.description.naturepostprint-
dc.identifier.doi10.1016/j.patcog.2011.09.027-
dc.identifier.scopuseid_2-s2.0-83655184804-
dc.identifier.hkuros201091en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-83655184804&selection=ref&src=s&origin=recordpage-
dc.identifier.volume45en_US
dc.identifier.issue4-
dc.identifier.spage1671en_US
dc.identifier.epage1683en_US
dc.identifier.isiWOS:000300459000036-
dc.publisher.placeNetherlands-
dc.identifier.scopusauthoridQin, J=24450951900-
dc.identifier.scopusauthoridYung, NHC=7003473369-

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