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Article: Rank-test similarity measure between video segments for local descriptors

TitleRank-test similarity measure between video segments for local descriptors
Authors
KeywordsDistributed Computer Systems
Distribution Functions
Feature Extraction
Integration
Statistical Methods
Issue Date2007
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
Lecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2007, v. 4398 LNCS, p. 71-81 How to Cite?
AbstractThis paper presents a novel and efficient similarity measure between video segments. We consider local spatio-temporal descriptors. They are considered to be realizations of an unknown, but class-specific distribution. The similarity of two video segments is calculated by evaluating an appropriate statistical criterion issued from a rank test. It does not require any matching of the local features between the two considered video segments, and can deal with a different number of computed local features in the two segments. Furthermore, our measure is self-normalized which allows for simple cue integration, and even on-line adapted class-dependent combination of the different descriptors. Satisfactory results have been obtained on real video sequences for two motion event recognition problems. © Springer-Verlag Berlin Heidelberg 2007.
Persistent Identifierhttp://hdl.handle.net/10722/132612
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252
References

 

DC FieldValueLanguage
dc.contributor.authorLehmann, Aen_HK
dc.contributor.authorBouthemy, Pen_HK
dc.contributor.authorYao, JFen_HK
dc.date.accessioned2011-03-28T09:27:00Z-
dc.date.available2011-03-28T09:27:00Z-
dc.date.issued2007en_HK
dc.identifier.citationLecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2007, v. 4398 LNCS, p. 71-81en_HK
dc.identifier.issn0302-9743en_HK
dc.identifier.urihttp://hdl.handle.net/10722/132612-
dc.description.abstractThis paper presents a novel and efficient similarity measure between video segments. We consider local spatio-temporal descriptors. They are considered to be realizations of an unknown, but class-specific distribution. The similarity of two video segments is calculated by evaluating an appropriate statistical criterion issued from a rank test. It does not require any matching of the local features between the two considered video segments, and can deal with a different number of computed local features in the two segments. Furthermore, our measure is self-normalized which allows for simple cue integration, and even on-line adapted class-dependent combination of the different descriptors. Satisfactory results have been obtained on real video sequences for two motion event recognition problems. © Springer-Verlag Berlin Heidelberg 2007.en_HK
dc.languageengen_US
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/en_HK
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_HK
dc.subjectDistributed Computer Systemsen_US
dc.subjectDistribution Functionsen_US
dc.subjectFeature Extractionen_US
dc.subjectIntegrationen_US
dc.subjectStatistical Methodsen_US
dc.titleRank-test similarity measure between video segments for local descriptorsen_HK
dc.typeArticleen_HK
dc.identifier.emailYao, JF: jeffyao@hku.hken_HK
dc.identifier.authorityYao, JF=rp01473en_HK
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-38049132529en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-38049132529&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume4398 LNCSen_HK
dc.identifier.spage71en_HK
dc.identifier.epage81en_HK
dc.identifier.eissn1611-3349-
dc.publisher.placeGermanyen_HK
dc.identifier.scopusauthoridLehmann, A=23393073600en_HK
dc.identifier.scopusauthoridBouthemy, P=7005146506en_HK
dc.identifier.scopusauthoridYao, JF=7403503451en_HK

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