Software Open Access

Visual Similarity Annotation Tool (V-SAT)

Eric Werner; Hussein Mohammed


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  <identifier identifierType="DOI">10.25592/uhhfdm.14851</identifier>
  <creators>
    <creator>
      <creatorName>Eric Werner</creatorName>
      <affiliation>Universität Hamburg</affiliation>
    </creator>
    <creator>
      <creatorName>Hussein Mohammed</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-5020-3592</nameIdentifier>
      <affiliation>Universität Hamburg</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Visual Similarity Annotation Tool (V-SAT)</title>
  </titles>
  <publisher>Universität Hamburg</publisher>
  <publicationYear>2024</publicationYear>
  <subjects>
    <subject>software, annotation tool, similarity measurement</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2024-09-10</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Software"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://www.fdr.uni-hamburg.de/record/14851</alternateIdentifier>
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    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsPartOf">10.25592/uhhfdm.14850</relatedIdentifier>
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  <version>1.0</version>
  <rightsList>
    <rights rightsURI="https://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;V-SAT is designed to streamline the organisation of image collections. Whether you have a structured folder hierarchy or a jumble of unsorted images, the tool simplifies the process. With features like similarity-based sorting and custom collection creation, users can quickly categorise their images. Once organised, the software enables users to export their curated collections. The main goal of this tool is to enable experts to create training datasets for similarity-based models such as siamese networks and supervised clustering.&lt;/p&gt;

&lt;p&gt;The research for this work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany&amp;rsquo;s Excellence Strategy - EXC 2176 &amp;lsquo;Understanding Written Artefacts: Material, Interaction and Transmission in Manuscript Cultures&amp;rsquo;, project no. 390893796. The research was conducted within the scope of the Centre for the Study of Manuscript Cultures (CSMC) at Universit&amp;auml;t Hamburg.&lt;/p&gt;</description>
  </descriptions>
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