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Editorial Image Retrieval using Handcrafted and CNN Features

Brito, Claudia Companioni; Elawady, Mohamed; Yildirim, Sule; Hardeberg, Jon Yngve
Journal article, Peer reviewed
Accepted version
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URI
http://hdl.handle.net/11250/2612365
Date
2018
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  • Institutt for datateknologi og informatikk [6323]
  • Institutt for informasjonssikkerhet og kommunikasjonsteknologi [2415]
  • Publikasjoner fra CRIStin - NTNU [34929]
Original version
Lecture Notes in Computer Science. 2018, 10884 LNCS 284-291.   10.1007/978-3-319-94211-7_31
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.
Publisher
Springer Verlag
Journal
Lecture Notes in Computer Science

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