1 Ocak 2014 Konferans bildirisi Açık Erişim

Ozkan, Savas; Ates, Tayfun; Tola, Engin; Soysal, Medeni; Esen, Ersin

Dublin Core

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  <dc:creator>Ozkan, Savas</dc:creator>
  <dc:creator>Ates, Tayfun</dc:creator>
  <dc:creator>Tola, Engin</dc:creator>
  <dc:creator>Soysal, Medeni</dc:creator>
  <dc:creator>Esen, Ersin</dc:creator>
  <dc:date>2014-01-01</dc:date>
  <dc:description>In this work, we survey the perormance of various feature encoding models for geographic image retrieval task Recently introduced Vector-of-Locally-Aggregated Descriptors (VLAD) and its Product Quantization encoded binary version VLAD-PQ are compared with the widely used Bag-of-Word (BoW) model. Evaluation results are shown on a publicly available 21-class LULC dataset. With experiments, it is shown that VLAD outperforms classical BoW representation albeit with some increases in the computation time. Additionally, VLAD-PQ results in similar retrieval performance with VLAD but requiring no more computational or memory resources are observed</dc:description>
  <dc:identifier>https://aperta.ulakbim.gov.trrecord/99477</dc:identifier>
  <dc:identifier>oai:zenodo.org:99477</dc:identifier>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by</dc:rights>
  <dc:title>FEATURE ENCODING MODELS FOR GEOGRAPHIC IMAGE RETRIEVAL AND CATEGORIZATION</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePaper</dc:type>
  <dc:type>publication-conferencepaper</dc:type>
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