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Description
  • As the volume of multimedia data available on internet is tremendously increasing, the content-based similarity search becomes a popular approach to multimedia retrieval. The most popular retrieval concept is the k nearest neighbor (kNN) search. For a long time, the kNN queries provided an effective retrieval in multimedia databases. However, as today's multimedia databases available on the web grow to massive volumes, the classic kNN query quickly loses its descriptive power. In this paper, we introduce a new similarity query type, the k distinct nearest neighbors (kDNN), which aims to generalize the classic kNN query to be more robust with respect to the database size. In addition to retrieving just objects similar to the query example, the kDNN further ensures the objects within the result have to be distinct enough, i.e. excluding near duplicates.
  • As the volume of multimedia data available on internet is tremendously increasing, the content-based similarity search becomes a popular approach to multimedia retrieval. The most popular retrieval concept is the k nearest neighbor (kNN) search. For a long time, the kNN queries provided an effective retrieval in multimedia databases. However, as today's multimedia databases available on the web grow to massive volumes, the classic kNN query quickly loses its descriptive power. In this paper, we introduce a new similarity query type, the k distinct nearest neighbors (kDNN), which aims to generalize the classic kNN query to be more robust with respect to the database size. In addition to retrieving just objects similar to the query example, the kDNN further ensures the objects within the result have to be distinct enough, i.e. excluding near duplicates. (en)
Title
  • Distinct nearest neighbors queries for similarity search in very large multimedia databases
  • Distinct nearest neighbors queries for similarity search in very large multimedia databases (en)
skos:prefLabel
  • Distinct nearest neighbors queries for similarity search in very large multimedia databases
  • Distinct nearest neighbors queries for similarity search in very large multimedia databases (en)
skos:notation
  • RIV/00216224:14330/09:00029810!RIV10-GA0-14330___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA201/09/0683), P(GP201/07/P240), P(GP201/08/P507)
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
  • 310818
http://linked.open...ai/riv/idVysledku
  • RIV/00216224:14330/09:00029810
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • similarity search; kNN query; content-based retrieval (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [47C6695A7C71]
http://linked.open...v/mistoKonaniAkce
  • Hong Kong, China
http://linked.open...i/riv/mistoVydani
  • New York, USA
http://linked.open...i/riv/nazevZdroje
  • 11th ACM International Workshop on Web Information and Data Management (WIDM 2009)
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
  • Skopal, Tomáš
  • Batko, Michal
  • Zezula, Pavel
  • Dohnal, Vlastislav
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • ACM
https://schema.org/isbn
  • 978-1-60558-808-7
http://localhost/t...ganizacniJednotka
  • 14330
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