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ITI-CERTH participation in TRECVID 2017

Markatopoulou, Foteini; Moumtzidou, Anastasia; Galanopoulos, Damianos; Avgerinakis, Konstantinos; Andreadis, Stelios; Gialampoukidis, Ilias; Tachos, Stavros; Vrochidis, Stefanos; Mezaris, Vasileios; Kompatsiaris, Ioannis; Patras, Ioannis


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  <dc:creator>Markatopoulou, Foteini</dc:creator>
  <dc:creator>Moumtzidou, Anastasia</dc:creator>
  <dc:creator>Galanopoulos, Damianos</dc:creator>
  <dc:creator>Avgerinakis, Konstantinos</dc:creator>
  <dc:creator>Andreadis, Stelios</dc:creator>
  <dc:creator>Gialampoukidis, Ilias</dc:creator>
  <dc:creator>Tachos, Stavros</dc:creator>
  <dc:creator>Vrochidis, Stefanos</dc:creator>
  <dc:creator>Mezaris, Vasileios</dc:creator>
  <dc:creator>Kompatsiaris, Ioannis</dc:creator>
  <dc:creator>Patras, Ioannis</dc:creator>
  <dc:date>2018-02-23</dc:date>
  <dc:description>This paper provides an overview of the runs submitted to TRECVID 2017 by ITI-CERTH. ITI-CERTH participated in the Ad-hoc Video Search (AVS), Multimedia Event Detection (MED), Instance Search (INS) and Surveillance Event Detection (SED) tasks. Our AVS task participation is based on a method that combines the linguistic analysis of the query with concept-based and semantic-embedding representations of video fragments. Regarding the MED task, this year we participate on Pre-Specied and Ah-Hoc sub-tasks exploiting both motion-based as well as DCNN-based features. The INS task is performed by employing VERGE, which is an interactive retrieval application that integrates retrieval functionalities that consider mainly visual information. For the SED task, we deploy a novel activity detection algorithm that is based on human detection in video frames, goal descriptors, dense trajectories, Fisher vectors and a discriminative action segmentation scheme.</dc:description>
  <dc:identifier>https://zenodo.org/record/1183440</dc:identifier>
  <dc:identifier>10.5281/zenodo.1183440</dc:identifier>
  <dc:identifier>oai:zenodo.org:1183440</dc:identifier>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/700475/</dc:relation>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/700024/</dc:relation>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/687786/</dc:relation>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/732665/</dc:relation>
  <dc:relation>doi:10.5281/zenodo.1183439</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/emma-h2020</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/invid-h2020</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:title>ITI-CERTH participation in TRECVID 2017</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePaper</dc:type>
  <dc:type>publication-conferencepaper</dc:type>
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