1 Ocak 2017 Dergi makalesi Açık Erişim

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

Dublin Core

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  <dc:creator>Kilickaya, Mert</dc:creator>
  <dc:creator>Akkus, Burak Kerim</dc:creator>
  <dc:creator>Cakici, Ruket</dc:creator>
  <dc:creator>Erdem, Aykut</dc:creator>
  <dc:creator>Erdem, Erkut</dc:creator>
  <dc:creator>Ikizler-Cinbis, Nazli</dc:creator>
  <dc:date>2017-01-01</dc:date>
  <dc:description>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.</dc:description>
  <dc:identifier>https://aperta.ulakbim.gov.trrecord/46249</dc:identifier>
  <dc:identifier>oai:zenodo.org:46249</dc:identifier>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by</dc:rights>
  <dc:source>IET COMPUTER VISION 11(6) 398-406</dc:source>
  <dc:title>Data-driven image captioning via salient region discovery</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
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