1 Ocak 2017 Dergi makalesi Açık Erişim

Kilickaya, Mert; Akkus, Burak Kerim; Cakici, Ruket; Erdem, Aykut; Erdem, Erkut; Ikizler-Cinbis, Nazli

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/46249</identifier>
  <creators>
    <creator>
      <creatorName>Kilickaya, Mert</creatorName>
      <givenName>Mert</givenName>
      <familyName>Kilickaya</familyName>
      <affiliation>Hacettepe Univ, Dept Comp Engn, Ankara, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Akkus, Burak Kerim</creatorName>
      <givenName>Burak Kerim</givenName>
      <familyName>Akkus</familyName>
      <affiliation>Middle East Tech Univ, Dept Comp Engn, Ankara, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Cakici, Ruket</creatorName>
      <givenName>Ruket</givenName>
      <familyName>Cakici</familyName>
      <affiliation>Middle East Tech Univ, Dept Comp Engn, Ankara, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Erdem, Aykut</creatorName>
      <givenName>Aykut</givenName>
      <familyName>Erdem</familyName>
      <affiliation>Hacettepe Univ, Dept Comp Engn, Ankara, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Erdem, Erkut</creatorName>
      <givenName>Erkut</givenName>
      <familyName>Erdem</familyName>
      <affiliation>Hacettepe Univ, Dept Comp Engn, Ankara, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Ikizler-Cinbis, Nazli</creatorName>
      <givenName>Nazli</givenName>
      <familyName>Ikizler-Cinbis</familyName>
      <affiliation>Hacettepe Univ, Dept Comp Engn, Ankara, Turkey</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Data-Driven Image Captioning Via Salient Region Discovery</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2017</publicationYear>
  <dates>
    <date dateType="Issued">2017-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/46249</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1049/iet-cvi.2016.0286</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">In the past few years, automatically generating descriptions for images has attracted a lot of attention in computer vision and natural language processing research. Among the existing approaches, data-driven methods have been proven to be highly effective. These methods compare the given image against a large set of training images to determine a set of relevant images, then generate a description using the associated captions. In this study, the authors propose to integrate an object-based semantic image representation into a deep features-based retrieval framework to select the relevant images. Moreover, they present a novel phrase selection paradigm and a sentence generation model which depends on a joint analysis of salient regions in the input and retrieved images within a clustering framework. The authors demonstrate the effectiveness of their proposed approach on Flickr8K and Flickr30K benchmark datasets and show that their model gives highly competitive results compared with the state-of-the-art models.</description>
  </descriptions>
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