Conference paper Open Access

Generic to Specific Recognition Models for Membership Analysis in Group Videos

Wenxuan Mou; Christos Tzelepis; Vasileios Mezaris


MARC21 XML Export

<?xml version='1.0' encoding='UTF-8'?>
<record xmlns="http://www.loc.gov/MARC21/slim">
  <leader>00000nam##2200000uu#4500</leader>
  <datafield tag="041" ind1=" " ind2=" ">
    <subfield code="a">eng</subfield>
  </datafield>
  <controlfield tag="005">20200120170340.0</controlfield>
  <controlfield tag="001">1135101</controlfield>
  <datafield tag="711" ind1=" " ind2=" ">
    <subfield code="d">30-3 June 2017</subfield>
    <subfield code="g">FG 2017</subfield>
    <subfield code="a">12th IEEE International Conference on Automatic Face &amp; Gesture Recognition</subfield>
    <subfield code="c">Washington, DC, USA</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Queen Mary, Univ. of London</subfield>
    <subfield code="a">Christos Tzelepis</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Centre for Res. &amp; Technol. Hellas, Inf. Technol. Inst.</subfield>
    <subfield code="a">Vasileios Mezaris</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="s">1588552</subfield>
    <subfield code="z">md5:4084e6a219b933ae0c474447a299a361</subfield>
    <subfield code="u">https://zenodo.org/record/1135101/files/fg17_preprint.pdf</subfield>
  </datafield>
  <datafield tag="542" ind1=" " ind2=" ">
    <subfield code="l">open</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="y">Conference website</subfield>
    <subfield code="u">http://www.fg2017.org/</subfield>
  </datafield>
  <datafield tag="260" ind1=" " ind2=" ">
    <subfield code="c">2017-06-30</subfield>
  </datafield>
  <datafield tag="909" ind1="C" ind2="O">
    <subfield code="p">openaire</subfield>
    <subfield code="p">user-moving-h2020</subfield>
    <subfield code="o">oai:zenodo.org:1135101</subfield>
  </datafield>
  <datafield tag="100" ind1=" " ind2=" ">
    <subfield code="u">Queen Mary, Univ. of London</subfield>
    <subfield code="a">Wenxuan Mou</subfield>
  </datafield>
  <datafield tag="245" ind1=" " ind2=" ">
    <subfield code="a">Generic to Specific Recognition Models for Membership Analysis in Group Videos</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">user-moving-h2020</subfield>
  </datafield>
  <datafield tag="536" ind1=" " ind2=" ">
    <subfield code="c">693092</subfield>
    <subfield code="a">Training towards a society of data-savvy information professionals to enable open leadership innovation</subfield>
  </datafield>
  <datafield tag="540" ind1=" " ind2=" ">
    <subfield code="u">https://creativecommons.org/licenses/by/4.0/legalcode</subfield>
    <subfield code="a">Creative Commons Attribution 4.0 International</subfield>
  </datafield>
  <datafield tag="650" ind1="1" ind2="7">
    <subfield code="a">cc-by</subfield>
    <subfield code="2">opendefinition.org</subfield>
  </datafield>
  <datafield tag="520" ind1=" " ind2=" ">
    <subfield code="a">&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;</subfield>
  </datafield>
  <datafield tag="024" ind1=" " ind2=" ">
    <subfield code="a">10.1109/FG.2017.69</subfield>
    <subfield code="2">doi</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">publication</subfield>
    <subfield code="b">conferencepaper</subfield>
  </datafield>
</record>
45
63
views
downloads
Views 45
Downloads 63
Data volume 100.1 MB
Unique views 35
Unique downloads 61

Share

Cite as