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Statements

Subject Item
n2:RIV%2F00216305%3A26230%2F09%3APU82627%21RIV10-MSM-26230___
rdf:type
skos:Concept n15:Vysledek
dcterms:description
The paper deals with an application of clustering we used as one of data reduction methods included in processing huge amount of video data provided for TRECVid evaluations. The problem we solved by means of clustering was to partition the local feature descriptors space so that thousands of partitions represent visual words, which may be effectively employed in video retrieval using classical information retrieval techniques. It has proved that well-known algorithms as K-means do not work well in this task or their computational complexity is too high. Therefore we developed a simple clustering method (referred to as MLD) that partitions the high-dimensional feature space incrementally in one to two database scans. The paper describes the problem of video retrieval and the role of clustering in the process, the MLD method and experiments focused on comparison with other clustering methods in the video retrieval application context. The paper deals with an application of clustering we used as one of data reduction methods included in processing huge amount of video data provided for TRECVid evaluations. The problem we solved by means of clustering was to partition the local feature descriptors space so that thousands of partitions represent visual words, which may be effectively employed in video retrieval using classical information retrieval techniques. It has proved that well-known algorithms as K-means do not work well in this task or their computational complexity is too high. Therefore we developed a simple clustering method (referred to as MLD) that partitions the high-dimensional feature space incrementally in one to two database scans. The paper describes the problem of video retrieval and the role of clustering in the process, the MLD method and experiments focused on comparison with other clustering methods in the video retrieval application context.
dcterms:title
Clustering for Video Retrieval Clustering for Video Retrieval
skos:prefLabel
Clustering for Video Retrieval Clustering for Video Retrieval
skos:notation
RIV/00216305:26230/09:PU82627!RIV10-MSM-26230___
n3:aktivita
n14:Z
n3:aktivity
Z(MSM0021630528)
n3:dodaniDat
n8:2010
n3:domaciTvurceVysledku
n4:8702411 n4:3725340 n4:4652738
n3:druhVysledku
n13:D
n3:duvernostUdaju
n17:S
n3:entitaPredkladatele
n10:predkladatel
n3:idSjednocenehoVysledku
307354
n3:idVysledku
RIV/00216305:26230/09:PU82627
n3:jazykVysledku
n11:eng
n3:klicovaSlova
Incremental clustering, MLD, Leader, ART, video retrieval, feature extraction, SURF, MSER, SIFT, cosine distance.
n3:klicoveSlovo
n6:MSER n6:feature%20extraction n6:Incremental%20clustering n6:SURF n6:SIFT n6:cosine%20distance. n6:ART n6:Leader n6:video%20retrieval n6:MLD
n3:kontrolniKodProRIV
[8018002486A2]
n3:mistoKonaniAkce
Linz
n3:mistoVydani
Heidelberg
n3:nazevZdroje
Data Warehousing and Knowledge Discovery
n3:obor
n9:JC
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:rokUplatneniVysledku
n8:2009
n3:tvurceVysledku
Chmelař, Petr Rudolfová, Ivana Zendulka, Jaroslav
n3:typAkce
n18:WRD
n3:zahajeniAkce
2009-08-31+02:00
n3:zamer
n21:MSM0021630528
s:numberOfPages
12
n19:hasPublisher
Springer-Verlag
n12:isbn
978-3-642-03729-0
n20:organizacniJednotka
26230