Journal article Open Access

Automatic synchronization of multi-user photo galleries

Sansone, Emanuele; Apostolidis, Konstantinos; Conci, Nicola; Boato, Giulia; Mezaris, Vasileios; De Natale, Francesco G.B.


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  <identifier identifierType="URL">https://zenodo.org/record/322380</identifier>
  <creators>
    <creator>
      <creatorName>Sansone, Emanuele</creatorName>
      <givenName>Emanuele</givenName>
      <familyName>Sansone</familyName>
      <affiliation>Department of Information Engineering and Computer Science - DISI, University of Trento</affiliation>
    </creator>
    <creator>
      <creatorName>Apostolidis, Konstantinos</creatorName>
      <givenName>Konstantinos</givenName>
      <familyName>Apostolidis</familyName>
      <affiliation>Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH)</affiliation>
    </creator>
    <creator>
      <creatorName>Conci, Nicola</creatorName>
      <givenName>Nicola</givenName>
      <familyName>Conci</familyName>
      <affiliation>Department of Information Engineering and Computer Science - DISI, University of Trento</affiliation>
    </creator>
    <creator>
      <creatorName>Boato, Giulia</creatorName>
      <givenName>Giulia</givenName>
      <familyName>Boato</familyName>
      <affiliation>Department of Information Engineering and Computer Science - DISI, University of Trento</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>De Natale, Francesco G.B.</creatorName>
      <givenName>Francesco G.B.</givenName>
      <familyName>De Natale</familyName>
      <affiliation>Department of Information Engineering and Computer Science - DISI, University of Trento</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Automatic synchronization of multi-user photo galleries</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2017</publicationYear>
  <subjects>
    <subject>Markov Networks</subject>
    <subject>Weighted Graph</subject>
    <subject>Multimodal</subject>
    <subject>Multimedia Synchronization</subject>
    <subject>Events</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2017-01-18</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/322380</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/TMM.2017.2655446</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/invid-h2020</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://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 address the issue of photo galleries synchronization, where pictures related to the same event are collected by different users. Existing solutions to address the problem are usually based on unrealistic assumptions, like time consistency across photo galleries, and often heavily rely on heuristics, limiting therefore the applicability to real-world scenarios. We propose a solution that achieves better generalization performance for the synchronization task compared to the available literature. The method is characterized by three stages: at first, deep convolutional neural network features are used to assess the visual similarity among the photos; then, pairs of similar photos are detected across different galleries and used to construct a graph; eventually, a probabilistic graphical model is used to estimate the temporal offset of each pair of galleries, by traversing the minimum spanning tree extracted&lt;br&gt;
from this graph. The experimental evaluation is conducted on four publicly available datasets covering different types of events, demonstrating the strength of our proposed method. A thorough discussion of the obtained results is provided for a critical assessment of the quality in synchronization.&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/H2020/687786/">687786</awardNumber>
      <awardTitle>In Video Veritas – Verification of Social Media Video Content for the News Industry</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
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