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Statements

Subject Item
n2:RIV%2F67985556%3A_____%2F08%3A00317590%21RIV10-AV0-67985556
rdf:type
skos:Concept n19:Vysledek
dcterms:description
With significantly increasing number of archived movie sequences a need of their automatic indexation and annotation is raising. Robust and fast temporal segmentation of video sequences is one of the challenging research topics in this area. In this paper we propose a new temporal segmentation method of the video sequences based on PCA approach. Contrary to standard approaches based on histogram or motion field analysis the proposed method does not require any such a complex analysis. The method starts with sparse greyscale sampling and eigen-analysis of input sequence. A sum of absolute derivatives of temporal mixing coefficients of main eigen-images is then used as cuts detection feature, while dissolve transitions are detected by means of coefficients' specific behaviour. The functionality of the method was successfully tested on number of sequences ranging from artificial set of similar dynamic textures to professional documentary movies. With significantly increasing number of archived movie sequences a need of their automatic indexation and annotation is raising. Robust and fast temporal segmentation of video sequences is one of the challenging research topics in this area. In this paper we propose a new temporal segmentation method of the video sequences based on PCA approach. Contrary to standard approaches based on histogram or motion field analysis the proposed method does not require any such a complex analysis. The method starts with sparse greyscale sampling and eigen-analysis of input sequence. A sum of absolute derivatives of temporal mixing coefficients of main eigen-images is then used as cuts detection feature, while dissolve transitions are detected by means of coefficients' specific behaviour. The functionality of the method was successfully tested on number of sequences ranging from artificial set of similar dynamic textures to professional documentary movies.
dcterms:title
Fast and Reliable PCA-Based Temporal Segmentation of Video Sequences Fast and Reliable PCA-Based Temporal Segmentation of Video Sequences
skos:prefLabel
Fast and Reliable PCA-Based Temporal Segmentation of Video Sequences Fast and Reliable PCA-Based Temporal Segmentation of Video Sequences
skos:notation
RIV/67985556:_____/08:00317590!RIV10-AV0-67985556
n3:aktivita
n12:P n12:Z
n3:aktivity
P(1ET400750407), P(GA102/08/0593), Z(AV0Z10750506)
n3:dodaniDat
n4:2010
n3:domaciTvurceVysledku
n7:2007673 n7:2890542
n3:druhVysledku
n6:D
n3:duvernostUdaju
n16:S
n3:entitaPredkladatele
n15:predkladatel
n3:idSjednocenehoVysledku
367683
n3:idVysledku
RIV/67985556:_____/08:00317590
n3:jazykVysledku
n9:eng
n3:klicovaSlova
video segmentation
n3:klicoveSlovo
n21:video%20segmentation
n3:kontrolniKodProRIV
[B84156AE45C1]
n3:mistoKonaniAkce
Tampa
n3:mistoVydani
Los Alamitos
n3:nazevZdroje
Proceedings of the 19th International Conference on Pattern Recognition
n3:obor
n14:BD
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n11:GA102%2F08%2F0593 n11:1ET400750407
n3:rokUplatneniVysledku
n4:2008
n3:tvurceVysledku
Haindl, Michal Filip, Jiří
n3:typAkce
n8:WRD
n3:zahajeniAkce
2008-12-07+01:00
n3:zamer
n13:AV0Z10750506
s:numberOfPages
4
n18:hasPublisher
IEEE Press
n20:isbn
978-1-4244-2174-9