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Statements

Subject Item
n2:RIV%2F00216224%3A14330%2F09%3A00029810%21RIV10-GA0-14330___
rdf:type
n13:Vysledek skos:Concept
dcterms: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.
dcterms: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
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
skos:notation
RIV/00216224:14330/09:00029810!RIV10-GA0-14330___
n3:aktivita
n11:P
n3:aktivity
P(GA201/09/0683), P(GP201/07/P240), P(GP201/08/P507)
n3:dodaniDat
n6:2010
n3:domaciTvurceVysledku
n4:3165647 n4:3540324 n4:8876398
n3:druhVysledku
n17:D
n3:duvernostUdaju
n7:S
n3:entitaPredkladatele
n15:predkladatel
n3:idSjednocenehoVysledku
310818
n3:idVysledku
RIV/00216224:14330/09:00029810
n3:jazykVysledku
n14:eng
n3:klicovaSlova
similarity search; kNN query; content-based retrieval
n3:klicoveSlovo
n12:content-based%20retrieval n12:kNN%20query n12:similarity%20search
n3:kontrolniKodProRIV
[47C6695A7C71]
n3:mistoKonaniAkce
Hong Kong, China
n3:mistoVydani
New York, USA
n3:nazevZdroje
11th ACM International Workshop on Web Information and Data Management (WIDM 2009)
n3:obor
n8:IN
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
4
n3:projekt
n16:GP201%2F08%2FP507 n16:GA201%2F09%2F0683 n16:GP201%2F07%2FP240
n3:rokUplatneniVysledku
n6:2009
n3:tvurceVysledku
Dohnal, Vlastislav Skopal, Tomáš Batko, Michal Zezula, Pavel
n3:typAkce
n20:WRD
n3:zahajeniAkce
2009-11-02+01:00
s:numberOfPages
4
n18:hasPublisher
ACM
n19:isbn
978-1-60558-808-7
n10:organizacniJednotka
14330