Conference paper Open Access

Query and Keyframe Representations for Ad-hoc Video Search

Markatopoulou, Foteini; Galanopoulos, Damianos; Mezaris, Vasileios; Patras, Ioannis


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  <identifier identifierType="URL">https://zenodo.org/record/809672</identifier>
  <creators>
    <creator>
      <creatorName>Markatopoulou, Foteini</creatorName>
      <givenName>Foteini</givenName>
      <familyName>Markatopoulou</familyName>
      <affiliation>Information Technologies Institute (ITI) - Centre for Research and Technology Hellas (CERTH)</affiliation>
    </creator>
    <creator>
      <creatorName>Galanopoulos, Damianos</creatorName>
      <givenName>Damianos</givenName>
      <familyName>Galanopoulos</familyName>
      <affiliation>Information Technologies Institute (ITI) - Centre for Research and Technology Hellas (CERTH)</affiliation>
    </creator>
    <creator>
      <creatorName>Mezaris, Vasileios</creatorName>
      <givenName>Vasileios</givenName>
      <familyName>Mezaris</familyName>
      <affiliation>Information Technologies Institute (ITI) - Centre for Research and Technology Hellas (CERTH)</affiliation>
    </creator>
    <creator>
      <creatorName>Patras, Ioannis</creatorName>
      <givenName>Ioannis</givenName>
      <familyName>Patras</familyName>
      <affiliation>Queen Mary University of London</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Query and Keyframe Representations for Ad-hoc Video Search</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2017</publicationYear>
  <subjects>
    <subject>Video search</subject>
    <subject>Zero-shot learning</subject>
    <subject>Visual analysis</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2017-06-08</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/809672</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1145/3078971.3079041</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;This paper presents a fully-automatic method that combines video concept detection and textual query analysis in order to solve the problem of ad-hoc video search. We present a set of NLP steps that cleverly analyse different parts of the query in order to convert it to related semantic concepts, we propose a new method for transforming concept-based keyframe and query representations into a common semantic embedding space, and we show that our proposed combination of concept-based representations with their corresponding semantic embeddings results to improved video search accuracy. Our experiments in the TRECVID AVS 2016 and the Video Search 2008 datasets show the effectiveness of the proposed method compared to other similar approaches.&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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