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Statements

Subject Item
n2:RIV%2F00216305%3A26220%2F13%3APU104500%21RIV14-MPO-26220___
rdf:type
n7:Vysledek skos:Concept
dcterms:description
Video scene segmentation is a process for dividing video into semantically meaningful blocks. This can help e.g. search engines to divide video into better manageable parts and enable more relevant search in video. Unfortunately, scene segmentation is based on the semantic and therefore it is a difficult task for computers. This work is preliminary study involved into supervised video scene segmentation, which is driven by the way how human segments scenes in a movie. Since these video segments represent semantic parts in video, it can be used for better video annotation and also for searching in videos. As a training set, only high quality movies were used and from these movies 100 training samples have been extracted and used for evaluation. Resulting model is a method based on general color layout, Tamura similarity measure and k-nearest neighbors achieving 97.00% accuracy. Video scene segmentation is a process for dividing video into semantically meaningful blocks. This can help e.g. search engines to divide video into better manageable parts and enable more relevant search in video. Unfortunately, scene segmentation is based on the semantic and therefore it is a difficult task for computers. This work is preliminary study involved into supervised video scene segmentation, which is driven by the way how human segments scenes in a movie. Since these video segments represent semantic parts in video, it can be used for better video annotation and also for searching in videos. As a training set, only high quality movies were used and from these movies 100 training samples have been extracted and used for evaluation. Resulting model is a method based on general color layout, Tamura similarity measure and k-nearest neighbors achieving 97.00% accuracy.
dcterms:title
Supervised Video Scene Segmentation using Similarity Measures Supervised Video Scene Segmentation using Similarity Measures Supervised Video Scene Segmentation using Similarity Measures Supervised Video Scene Segmentation using Similarity Measures
skos:prefLabel
Supervised Video Scene Segmentation using Similarity Measures Supervised Video Scene Segmentation using Similarity Measures Supervised Video Scene Segmentation using Similarity Measures Supervised Video Scene Segmentation using Similarity Measures
skos:notation
RIV/00216305:26220/13:PU104500!RIV14-MPO-26220___
n7:predkladatel
n8:orjk%3A26220
n3:aktivita
n9:S n9:P
n3:aktivity
P(FR-TI4/151), S
n3:dodaniDat
n19:2014
n3:domaciTvurceVysledku
n10:2629291 n10:9747397 n10:8261571
n3:druhVysledku
n14:D
n3:duvernostUdaju
n22:S
n3:entitaPredkladatele
n16:predkladatel
n3:idSjednocenehoVysledku
109023
n3:idVysledku
RIV/00216305:26220/13:PU104500
n3:jazykVysledku
n17:eng
n3:klicovaSlova
image analysis, machine learning, similarity measure, video segmentation.
n3:klicoveSlovo
n6:machine%20learning n6:similarity%20measure n6:video%20segmentation. n6:image%20analysis
n3:kontrolniKodProRIV
[251F39667BAD]
n3:mistoKonaniAkce
Rome
n3:mistoVydani
Neuveden
n3:nazevZdroje
36th International Conference on Telecommunications and Signal processing
n3:obor
n15:IN
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
4
n3:projekt
n21:FR-TI4%2F151
n3:rokUplatneniVysledku
n19:2013
n3:tvurceVysledku
Mašek, Jan Burget, Radim Uher, Václav Dutta, Malay Kishore
n3:typAkce
n18:WRD
n3:zahajeniAkce
2013-07-02+02:00
s:numberOfPages
912
n12:hasPublisher
Neuveden
n5:isbn
978-1-4799-0402-0
n20:organizacniJednotka
26220