Conference paper Open Access

Image Aesthetics Assessment using Fully Convolutional Neural Networks

Apostolidis, Konstantinos; Mezaris, Vasileios


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  <dc:creator>Apostolidis, Konstantinos</dc:creator>
  <dc:creator>Mezaris, Vasileios</dc:creator>
  <dc:date>2019-01-10</dc:date>
  <dc:description>This paper presents a new method for assessing the aesthetic quality of images. Based on the findings of previous works on this topic, we propose a method that addresses the shortcomings of existing ones, by: a) Making possible to feed higher-resolution images in the network, by introducing a fully convolutional neural network as the classier. b) Maintaining the original aspect ratio of images in the input of the network, to avoid distortions caused by re-scaling. And c) combining local and global features from the image for making the assessment of its aesthetic quality. The proposed method is shown to achieve state of the art results on a standard large-scale benchmark dataset.</dc:description>
  <dc:identifier>https://zenodo.org/record/2539133</dc:identifier>
  <dc:identifier>10.1007/978-3-030-05710-7_30</dc:identifier>
  <dc:identifier>oai:zenodo.org:2539133</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/687786/</dc:relation>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/732665/</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>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>Image Aesthetics</dc:subject>
  <dc:subject>Deep Learning</dc:subject>
  <dc:subject>Fully Convolutional Neural Networks</dc:subject>
  <dc:title>Image Aesthetics Assessment using Fully Convolutional Neural Networks</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePaper</dc:type>
  <dc:type>publication-conferencepaper</dc:type>
</oai_dc:dc>
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