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Description
  • In all subelds of information retrieval, test datasets and ground truth data are important tools for testing and comparison of new search methods. This is also reflected by the image retrieval community where several benchmarking activities have been created in past years using different test data. However, the number of available test collections is still rather small and the existing ones are often limited in size or accessible only to the participants of benchmarking competitions. In this work, we present a new freely-available large-scale dataset for evaluation of content-based image retrieval systems. The dataset consists of 20 million high-quality images with ve visual descriptors and rich and systematic textual annotations, a set of 100 test query objects and a semi-automatically collected ground truth data veried by users. Furthermore, we provide services that enable exploitation and collaborative expansion of the ground truth.
  • In all subelds of information retrieval, test datasets and ground truth data are important tools for testing and comparison of new search methods. This is also reflected by the image retrieval community where several benchmarking activities have been created in past years using different test data. However, the number of available test collections is still rather small and the existing ones are often limited in size or accessible only to the participants of benchmarking competitions. In this work, we present a new freely-available large-scale dataset for evaluation of content-based image retrieval systems. The dataset consists of 20 million high-quality images with ve visual descriptors and rich and systematic textual annotations, a set of 100 test query objects and a semi-automatically collected ground truth data veried by users. Furthermore, we provide services that enable exploitation and collaborative expansion of the ground truth. (en)
Title
  • Evaluation Platform for Content-based Image Retrieval Systems
  • Evaluation Platform for Content-based Image Retrieval Systems (en)
skos:prefLabel
  • Evaluation Platform for Content-based Image Retrieval Systems
  • Evaluation Platform for Content-based Image Retrieval Systems (en)
skos:notation
  • RIV/00216224:14330/11:00049925!RIV12-MV0-14330___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GAP103/10/0886), P(VF20102014004), S
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 198423
http://linked.open...ai/riv/idVysledku
  • RIV/00216224:14330/11:00049925
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • large-scale image dataset; visual and textual annotation; ground truth; collaboration service (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [4423A58EED26]
http://linked.open...v/mistoKonaniAkce
  • Berlin, Germany
http://linked.open...i/riv/mistoVydani
  • Berlin
http://linked.open...i/riv/nazevZdroje
  • International Conference on Theory and Practice of Digital Libraries 2011, LNCS 6966
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Batko, Michal
  • Budíková, Petra
  • Zezula, Pavel
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • Springer-Verlag
https://schema.org/isbn
  • 978-3-642-24468-1
http://localhost/t...ganizacniJednotka
  • 14330
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