Conference paper Open Access

Generic to Specific Recognition Models for Membership Analysis in Group Videos

Wenxuan Mou; Christos Tzelepis; Vasileios Mezaris

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      <creatorName>Wenxuan Mou</creatorName>
      <affiliation>Queen Mary, Univ. of London</affiliation>
      <creatorName>Christos Tzelepis</creatorName>
      <affiliation>Queen Mary, Univ. of London</affiliation>
      <creatorName>Vasileios Mezaris</creatorName>
      <affiliation>Centre for Res. &amp; Technol. Hellas, Inf. Technol. Inst.</affiliation>
    <title>Generic to Specific Recognition Models for Membership Analysis in Group Videos</title>
    <date dateType="Issued">2017-06-30</date>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
    <alternateIdentifier alternateIdentifierType="url"></alternateIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/FG.2017.69</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf"></relatedIdentifier>
    <rights rightsURI="">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;Automatic understanding and analysis of groups has attracted increasing attention in the vision and multimedia communities in recent years. However, little attention has been paid to the automatic analysis of group membership - i.e., recognizing which group the individual in question is part of. This paper presents a novel two-phase Support Vector Machine (SVM) based specific recognition model that is learned using an optimized generic recognition model. We conduct a set of experiments using a database collected to study group analysis from multimodal cues while each group (i.e., four participants together) were watching a number of long movie segments. Our experimental results show that the proposed specific recognition model (52%) outperforms the generic recognition model trained across all different videos (35%) and the independent recognition model trained directly on each specific video (33%) using linear SVM.&lt;/p&gt;</description>
      <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>
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