Shot retrieval based on fuzzy evolutionary aiNet and hybrid features

Kurzanzeige
dc.contributor.authorLi, Xiang-Huideu
dc.contributor.authorZhan, Yong-Zhaodeu
dc.contributor.authorKe, Jiadeu
dc.contributor.authorZheng, Hongwei
dc.date.accessioned2011-11-08T17:12:49Zdeu
dc.date.available2012-09-30T22:25:05Zdeu
dc.date.issued2011
dc.description.abstractAs the multimedia data increasing exponentially, how to get the video data we need efficiently become so important and urgent. In this paper, a novel method for shot retrieval is proposed, which is based on fuzzy evolutionary aiNet and hybrid features. To begin with, the fuzzy evolutionary aiNet algorithm proposed in this paper is utilized to extract key-frames in a video sequence. Meanwhile, to represent a key-frame, hybrid features of color feature, texture feature and spatial structure feature are extracted. Then, the features of key-frames in the same shot are taken as an ensemble and mapped to high dimension space by non-linear mapping, and the result obeys Gaussian distribution. Finally, shot similarity is measured by the probabilistic distance between distributions of the key-frame feature ensembles for two shots, and similar shots are retrieved effectively by using this method. Experimental results show the validity of this proposed method.eng
dc.description.versionpublished
dc.identifier.citationComputers in Human Behavior ; 27 (2011), 5. - S. 1571-1578deu
dc.identifier.doi10.1016/j.chb.2010.11.002deu
dc.identifier.ppn360584411deu
dc.identifier.urihttp://kops.uni-konstanz.de/handle/123456789/16627
dc.language.isoengdeu
dc.legacy.dateIssued2011-11-08deu
dc.rightsterms-of-usedeu
dc.rights.urihttps://rightsstatements.org/page/InC/1.0/deu
dc.subjectShot retrievaldeu
dc.subjectFuzzy evolutionary aiNetdeu
dc.subjectHybrid featuresdeu
dc.subjectProbabilistic distancedeu
dc.subjectSimilarity measuredeu
dc.subjectKey-frame extractiondeu
dc.subject.ddc004deu
dc.titleShot retrieval based on fuzzy evolutionary aiNet and hybrid featureseng
dc.typeJOURNAL_ARTICLEdeu
dspace.entity.typePublication
kops.citation.bibtex
@article{Li2011retri-16627,
  year={2011},
  doi={10.1016/j.chb.2010.11.002},
  title={Shot retrieval based on fuzzy evolutionary aiNet and hybrid features},
  number={5},
  volume={27},
  issn={0747-5632},
  journal={Computers in Human Behavior},
  pages={1571--1578},
  author={Li, Xiang-Hui and Zhan, Yong-Zhao and Ke, Jia and Zheng, Hongwei}
}
kops.citation.iso690LI, Xiang-Hui, Yong-Zhao ZHAN, Jia KE, Hongwei ZHENG, 2011. Shot retrieval based on fuzzy evolutionary aiNet and hybrid features. In: Computers in Human Behavior. 2011, 27(5), pp. 1571-1578. ISSN 0747-5632. Available under: doi: 10.1016/j.chb.2010.11.002deu
kops.citation.iso690LI, Xiang-Hui, Yong-Zhao ZHAN, Jia KE, Hongwei ZHENG, 2011. Shot retrieval based on fuzzy evolutionary aiNet and hybrid features. In: Computers in Human Behavior. 2011, 27(5), pp. 1571-1578. ISSN 0747-5632. Available under: doi: 10.1016/j.chb.2010.11.002eng
kops.citation.rdf
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/16627">
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-11-08T17:12:49Z</dc:date>
    <dc:language>eng</dc:language>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/16627/2/shot_retrieval.pdf"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2012-09-30T22:25:05Z</dcterms:available>
    <dc:contributor>Zheng, Hongwei</dc:contributor>
    <dc:creator>Zheng, Hongwei</dc:creator>
    <dc:rights>terms-of-use</dc:rights>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:contributor>Zhan, Yong-Zhao</dc:contributor>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:contributor>Li, Xiang-Hui</dc:contributor>
    <dcterms:bibliographicCitation>Computers in Human Behavior ; 27 (2011), 5. - S. 1571-1578</dcterms:bibliographicCitation>
    <dc:creator>Ke, Jia</dc:creator>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/16627/2/shot_retrieval.pdf"/>
    <dcterms:title>Shot retrieval based on fuzzy evolutionary aiNet and hybrid features</dcterms:title>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:contributor>Ke, Jia</dc:contributor>
    <dc:creator>Li, Xiang-Hui</dc:creator>
    <dcterms:issued>2011</dcterms:issued>
    <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/16627"/>
    <dc:creator>Zhan, Yong-Zhao</dc:creator>
    <dcterms:abstract xml:lang="eng">As the multimedia data increasing exponentially, how to get the video data we need efficiently become so important and urgent. In this paper, a novel method for shot retrieval is proposed, which is based on fuzzy evolutionary aiNet and hybrid features. To begin with, the fuzzy evolutionary aiNet algorithm proposed in this paper is utilized to extract key-frames in a video sequence. Meanwhile, to represent a key-frame, hybrid features of color feature, texture feature and spatial structure feature are extracted. Then, the features of key-frames in the same shot are taken as an ensemble and mapped to high dimension space by non-linear mapping, and the result obeys Gaussian distribution. Finally, shot similarity is measured by the probabilistic distance between distributions of the key-frame feature ensembles for two shots, and similar shots are retrieved effectively by using this method. Experimental results show the validity of this proposed method.</dcterms:abstract>
  </rdf:Description>
</rdf:RDF>
kops.description.openAccessopenaccessgreen
kops.identifier.nbnurn:nbn:de:bsz:352-166272deu
kops.sourcefieldComputers in Human Behavior. 2011, <b>27</b>(5), pp. 1571-1578. ISSN 0747-5632. Available under: doi: 10.1016/j.chb.2010.11.002deu
kops.sourcefield.plainComputers in Human Behavior. 2011, 27(5), pp. 1571-1578. ISSN 0747-5632. Available under: doi: 10.1016/j.chb.2010.11.002deu
kops.sourcefield.plainComputers in Human Behavior. 2011, 27(5), pp. 1571-1578. ISSN 0747-5632. Available under: doi: 10.1016/j.chb.2010.11.002eng
kops.submitter.emailwiebke.knop@uni-konstanz.dedeu
relation.isAuthorOfPublication2d3dfb08-60c8-4ce5-9771-6caba0479ec1
relation.isAuthorOfPublication.latestForDiscovery2d3dfb08-60c8-4ce5-9771-6caba0479ec1
source.bibliographicInfo.fromPage1571
source.bibliographicInfo.issue5
source.bibliographicInfo.toPage1578
source.bibliographicInfo.volume27
source.identifier.issn0747-5632
source.periodicalTitleComputers in Human Behavior

Dateien

Originalbündel

Gerade angezeigt 1 - 1 von 1
Vorschaubild nicht verfügbar
Name:
shot_retrieval.pdf
Größe:
15 MB
Format:
Adobe Portable Document Format
shot_retrieval.pdf
shot_retrieval.pdfGröße: 15 MBDownloads: 833

Lizenzbündel

Gerade angezeigt 1 - 1 von 1
Vorschaubild nicht verfügbar
Name:
license.txt
Größe:
1.92 KB
Format:
Plain Text
Beschreibung:
license.txt
license.txtGröße: 1.92 KBDownloads: 0

Sammlungen

Informatik und Informationswissenschaft: Publikationen