January 8, 2019 Conference paper Open Access

Damianos Galanopoulos; Vasileios Mezaris

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  <identifier identifierType="DOI">10.5281/zenodo.2539272</identifier>
  <creators>
    <creator>
      <creatorName>Damianos Galanopoulos</creatorName>
      <affiliation>Information Technologies Institute/CERTH</affiliation>
    </creator>
    <creator>
      <creatorName>Vasileios Mezaris</creatorName>
      <affiliation>Information Technologies Institute/CERTH</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Temporal Lecture Video Fragmentation using Word Embeddings</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <subjects>
    <subject>Lecture  Video  Fragmentation,</subject>
    <subject>Word  Embeddings</subject>
    <subject>Video Segmentation</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2019-01-08</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/2539272</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.2539271</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/moving-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 work the problem of temporal video lecture fragmentation in meaningful parts is addressed. The visual content of lecture video can not be effectively used for this task due to its extremely homogeneous content. A new method for lecture video fragmentation in which only automatically generated speech transcripts of a video are exploited, is proposed. Contrary to previously proposed works that employ visual, audio and textual features and use time-consuming supervised methods which require annotated training data, we present a method that analyses the transcripts&amp;rsquo; text with the help of word embeddings that are generated from pre-trained state-of-the-art neural networks. Furthermore,we address a major problem of video lecture fragmentation research, which is the lack of large-scale datasets for evaluation, by presenting a new artificially- generated dataset of synthetic video lecture transcripts that we make publicly available. Experimental comparisons document the merit of the proposed approach.&lt;/p&gt;</description>
  </descriptions>
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