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  • The Content-based Photo Image Retrieval (CoPhIR) dataset is the largest available database of digital images with corresponding visual descriptors. It contains five MPEG-7 global descriptors extracted from more than 106 million images from Flickr photo-sharing system. In this paper, we analyze this dataset focusing on 1) efficiency of similarity-based indexing and searching and on 2) expressiveness of combination of the descriptors with respect to subjective perception of visual similarity. We treat the descriptors as metric spaces and then combine them into a multi-metric space. We analyze distance distributions of individual descriptors, measure intrinsic dimensionality of these datasets and statistically evaluate correlation between these descriptors. Further, we use two methods to assess subjective accuracy and satisfaction of similarity retrieval based on a combination of descriptors that is recommended for CoPhIR, and we compare these results on databases of 10 and 100 million CoPhIR images.
  • The Content-based Photo Image Retrieval (CoPhIR) dataset is the largest available database of digital images with corresponding visual descriptors. It contains five MPEG-7 global descriptors extracted from more than 106 million images from Flickr photo-sharing system. In this paper, we analyze this dataset focusing on 1) efficiency of similarity-based indexing and searching and on 2) expressiveness of combination of the descriptors with respect to subjective perception of visual similarity. We treat the descriptors as metric spaces and then combine them into a multi-metric space. We analyze distance distributions of individual descriptors, measure intrinsic dimensionality of these datasets and statistically evaluate correlation between these descriptors. Further, we use two methods to assess subjective accuracy and satisfaction of similarity retrieval based on a combination of descriptors that is recommended for CoPhIR, and we compare these results on databases of 10 and 100 million CoPhIR images. (en)
Title
  • CoPhIR Image Collection under the Microscope
  • CoPhIR Image Collection under the Microscope (en)
skos:prefLabel
  • CoPhIR Image Collection under the Microscope
  • CoPhIR Image Collection under the Microscope (en)
skos:notation
  • RIV/00216224:14330/09:00029662!RIV10-GA0-14330___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA201/09/0683)
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
  • 308339
http://linked.open...ai/riv/idVysledku
  • RIV/00216224:14330/09:00029662
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • metric space; MPEG-7; visual descriptors; CoPhIR dataset; dataset analysis (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [353FDADB7C6F]
http://linked.open...v/mistoKonaniAkce
  • Prague, Czech Republic
http://linked.open...i/riv/mistoVydani
  • Washington, DC, USA
http://linked.open...i/riv/nazevZdroje
  • Proceedings of the 2009 Second International Workshop on Similarity Search and Applications
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
  • Novák, David
  • Batko, Michal
  • Budíková, Petra
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • IEEE Computer Society
https://schema.org/isbn
  • 978-0-7695-3765-8
http://localhost/t...ganizacniJednotka
  • 14330
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