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VIDEO AESTHETIC QUALITY ASSESSMENT USING KERNEL SUPPORT VECTOR MACHINE WITH ISOTROPIC GAUSSIAN SAMPLE UNCERTAINTY (KSVM-IGSU)

Christos Tzelepis; Eftichia Mavridaki; Vasileios Mezaris; Ioannis Patras


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  <identifier identifierType="URL">https://zenodo.org/record/159236</identifier>
  <creators>
    <creator>
      <creatorName>Christos Tzelepis</creatorName>
      <affiliation>Information Technologies Institute (ITI), CERTH, Queen Mary University of London</affiliation>
    </creator>
    <creator>
      <creatorName>Eftichia Mavridaki</creatorName>
      <affiliation>Information Technologies Institute (ITI), CERTH</affiliation>
    </creator>
    <creator>
      <creatorName>Vasileios Mezaris</creatorName>
      <affiliation>Information Technologies Institute (ITI), CERTH</affiliation>
    </creator>
    <creator>
      <creatorName>Ioannis Patras</creatorName>
      <affiliation>Queen Mary University of London</affiliation>
    </creator>
  </creators>
  <titles>
    <title>VIDEO AESTHETIC QUALITY ASSESSMENT USING KERNEL SUPPORT VECTOR MACHINE WITH ISOTROPIC GAUSSIAN SAMPLE UNCERTAINTY (KSVM-IGSU)</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2016</publicationYear>
  <subjects>
    <subject>Video aesthetic quality assessment</subject>
    <subject>Rules of photography and cinematography</subject>
    <subject>Support vector machine</subject>
    <subject>Video representation uncertainty</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2016-09-25</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/159236</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/ICIP.2016.7532791</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/ecfunded</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/invid-h2020</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/moving-h2020</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;In this paper we propose a video aesthetic quality assessment method that combines the representation of each video according to a set of photographic and cinematographic rules, with the use of a learning method that takes the video representation's uncertainty into consideration. Specifically, our method exploits the information derived from both low- and high-level analysis of video layout, leading to a photo- and motion-based video representation scheme. Subsequently, a kernel Support Vector Machine (SVM) extension, the KSVM-iGSU, is trained to classify the videos and retrieve those of high aesthetic value. Experimental results on our large dataset verify the effectiveness of the proposed method. We also make publicly available our dataset, in order to facilitate research in the area of video aesthetic quality assessment.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/693092/">693092</awardNumber>
      <awardTitle>Training towards a society of data-savvy information professionals to enable open leadership innovation</awardTitle>
    </fundingReference>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/687786/">687786</awardNumber>
      <awardTitle>In Video Veritas – Verification of Social Media Video Content for the News Industry</awardTitle>
    </fundingReference>
  </fundingReferences>
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