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Article: Scene categorization via contextual visual words

TitleScene categorization via contextual visual words
Authors
KeywordsContext based vision
Contextual visual words
Pattern recognition
Scene categorization
Issue Date2010
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/pr
Citation
Pattern Recognition, 2010, v. 43 n. 5, p. 1874-1888 How to Cite?
AbstractIn this paper, we propose a novel scene categorization method based on contextual visual words. In the proposed method, we extend the traditional 'bags of visual words' model by introducing contextual information from the coarser scale and neighborhood regions to the local region of interest based on unsupervised learning. The introduced contextual information provides useful information or cue about the region of interest, which can reduce the ambiguity when employing visual words to represent the local regions. The improved visual words representation of the scene image is capable of enhancing the categorization performance. The proposed method is evaluated over three scene classification datasets, with 8, 13 and 15 scene categories, respectively, using 10-fold cross-validation. The experimental results show that the proposed method achieves 90.30%, 87.63% and 85.16% recognition success for Dataset 1, 2 and 3, respectively, which significantly outperforms the methods based on the visual words that only represent the local information in the statistical manner. We also compared the proposed method with three representative scene categorization methods. The result confirms the superiority of the proposed method. © 2009 Elsevier Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/124677
ISSN
2015 Impact Factor: 3.399
2015 SCImago Journal Rankings: 2.051
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorQin, Jen_HK
dc.contributor.authorYung, NHCen_HK
dc.date.accessioned2010-10-31T10:47:59Z-
dc.date.available2010-10-31T10:47:59Z-
dc.date.issued2010en_HK
dc.identifier.citationPattern Recognition, 2010, v. 43 n. 5, p. 1874-1888en_HK
dc.identifier.issn0031-3203en_HK
dc.identifier.urihttp://hdl.handle.net/10722/124677-
dc.description.abstractIn this paper, we propose a novel scene categorization method based on contextual visual words. In the proposed method, we extend the traditional 'bags of visual words' model by introducing contextual information from the coarser scale and neighborhood regions to the local region of interest based on unsupervised learning. The introduced contextual information provides useful information or cue about the region of interest, which can reduce the ambiguity when employing visual words to represent the local regions. The improved visual words representation of the scene image is capable of enhancing the categorization performance. The proposed method is evaluated over three scene classification datasets, with 8, 13 and 15 scene categories, respectively, using 10-fold cross-validation. The experimental results show that the proposed method achieves 90.30%, 87.63% and 85.16% recognition success for Dataset 1, 2 and 3, respectively, which significantly outperforms the methods based on the visual words that only represent the local information in the statistical manner. We also compared the proposed method with three representative scene categorization methods. The result confirms the superiority of the proposed method. © 2009 Elsevier Ltd. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/pren_HK
dc.relation.ispartofPattern Recognitionen_HK
dc.subjectContext based visionen_HK
dc.subjectContextual visual wordsen_HK
dc.subjectPattern recognitionen_HK
dc.subjectScene categorizationen_HK
dc.titleScene categorization via contextual visual wordsen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0031-3203&volume=43&spage=1874 &epage= 1888&date=2010&atitle=Scene+categorization+via+contextual+visual+wordsen_HK
dc.identifier.emailYung, NHC:nyung@eee.hku.hken_HK
dc.identifier.authorityYung, NHC=rp00226en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.patcog.2009.11.009en_HK
dc.identifier.scopuseid_2-s2.0-75749158448en_HK
dc.identifier.hkuros176242en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-75749158448&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume43en_HK
dc.identifier.issue5en_HK
dc.identifier.spage1874en_HK
dc.identifier.epage1888en_HK
dc.identifier.eissn1873-5142-
dc.identifier.isiWOS:000275615800013-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridQin, J=24450951900en_HK
dc.identifier.scopusauthoridYung, NHC=7003473369en_HK
dc.identifier.citeulike6408606-

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