Publikation:

Shot retrieval based on fuzzy evolutionary aiNet and hybrid features

Lade...
Vorschaubild

Dateien

shot_retrieval.pdf
shot_retrieval.pdfGröße: 15 MBDownloads: 759

Datum

2011

Autor:innen

Li, Xiang-Hui
Zhan, Yong-Zhao
Ke, Jia

Herausgeber:innen

Kontakt

ISSN der Zeitschrift

Electronic ISSN

ISBN

Bibliografische Daten

Verlag

Schriftenreihe

Auflagebezeichnung

ArXiv-ID

Internationale Patentnummer

Angaben zur Forschungsförderung

Projekt

Open Access-Veröffentlichung
Open Access Green
Core Facility der Universität Konstanz

Gesperrt bis

Titel in einer weiteren Sprache

Publikationstyp
Zeitschriftenartikel
Publikationsstatus
Published

Erschienen in

Computers in Human Behavior. 2011, 27(5), pp. 1571-1578. ISSN 0747-5632. Available under: doi: 10.1016/j.chb.2010.11.002

Zusammenfassung

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.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
004 Informatik

Schlagwörter

Shot retrieval, Fuzzy evolutionary aiNet, Hybrid features, Probabilistic distance, Similarity measure, Key-frame extraction

Konferenz

Rezension
undefined / . - undefined, undefined

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Zugehörige Datensätze in KOPS

Zitieren

ISO 690LI, 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.2010.11.002
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}
}
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>

Interner Vermerk

xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter

Kontakt
URL der Originalveröffentl.

Prüfdatum der URL

Prüfungsdatum der Dissertation

Finanzierungsart

Kommentar zur Publikation

Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Begutachtet
Diese Publikation teilen