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Editorial Image Retrieval Using Handcrafted and CNN Features

  • Claudia Companioni-BritoEmail author
  • Mohamed Elawady
  • Sule Yildirim
  • Jon Yngve Hardeberg
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10884)

Abstract

Textual keywords have been used in the early stages for image retrieval systems. Due to the huge increase of image content, an image is efficiently used instead according to the time computation. Deciding powerful feature representations are the important factors for the retrieval performance of a content-based image retrieval (CBIR) system. In this work, we present a combined feature representation based on handcrafted and deep approaches, to categorize editorial images into six classes (athletics, football, indoor, outdoor, portrait, ski). The experimental results show the superior performance of the combined features among different editorial classes.

Keywords

Image features Similarity CBIR CNN LBP BoVW 

Notes

Acknowledgments

I would like to thank NTB, the Norwegian news agency, for providing the dataset for research use, from the Scanpix database.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Claudia Companioni-Brito
    • 1
    Email author
  • Mohamed Elawady
    • 2
  • Sule Yildirim
    • 1
  • Jon Yngve Hardeberg
    • 1
  1. 1.NTNU - Norwegian University of Science and TechnologyGjøvikNorway
  2. 2.Université de Lyon, UJM-Saint-Etienne, CNRS, IOGS, Laboratoire Hubert Curien UMR5516Saint-EtienneFrance

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