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Statements

Subject Item
n2:RIV%2F00216305%3A26220%2F13%3APU104757%21RIV14-MPO-26220___
rdf:type
n7:Vysledek skos:Concept
dcterms:description
General image segmentation is a non–trivial task, which requires significant computational power and huge amount of knowledge incorporated. Fortunately, it is not necessary in all the cases. In some specific cases, simpler non–supervised or supervised segmentation methods can be used giving even better results. In this paper, a novel trainable segmentation method based on RapidMiner data–mining platform is introduced, and its functionality is described. The method implementation was released under open–source license as a part of IMMI (IMage MIning) extension of the RapidMiner platform. When compared to other trainable segmentation algorithms, the platform provides flexibility connected with all the features of one of the most widely used data–mining platform today. The functionality has been verified on the satellite image use–case, accuracy achieving 78.3% pixel error. General image segmentation is a non–trivial task, which requires significant computational power and huge amount of knowledge incorporated. Fortunately, it is not necessary in all the cases. In some specific cases, simpler non–supervised or supervised segmentation methods can be used giving even better results. In this paper, a novel trainable segmentation method based on RapidMiner data–mining platform is introduced, and its functionality is described. The method implementation was released under open–source license as a part of IMMI (IMage MIning) extension of the RapidMiner platform. When compared to other trainable segmentation algorithms, the platform provides flexibility connected with all the features of one of the most widely used data–mining platform today. The functionality has been verified on the satellite image use–case, accuracy achieving 78.3% pixel error.
dcterms:title
IMMI: Interactive Segmentation Toolkit IMMI: Interactive Segmentation Toolkit
skos:prefLabel
IMMI: Interactive Segmentation Toolkit IMMI: Interactive Segmentation Toolkit
skos:notation
RIV/00216305:26220/13:PU104757!RIV14-MPO-26220___
n7:predkladatel
n8:orjk%3A26220
n3:aktivita
n18:S n18:P
n3:aktivity
P(FR-TI4/151), S
n3:dodaniDat
n4:2014
n3:domaciTvurceVysledku
n20:9747397 n20:8261571 n20:2629291
n3:druhVysledku
n21:D
n3:duvernostUdaju
n11:S
n3:entitaPredkladatele
n16:predkladatel
n3:idSjednocenehoVysledku
78893
n3:idVysledku
RIV/00216305:26220/13:PU104757
n3:jazykVysledku
n19:eng
n3:klicovaSlova
Classification, image segmentation, interactive tool, IMMI, RapidMiner
n3:klicoveSlovo
n12:IMMI n12:Classification n12:interactive%20tool n12:RapidMiner n12:image%20segmentation
n3:kontrolniKodProRIV
[999D4AE38D90]
n3:mistoKonaniAkce
Halkidiki
n3:mistoVydani
Heidelberg
n3:nazevZdroje
Engineering Applications of Neural Networks
n3:obor
n10:IN
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n5:FR-TI4%2F151
n3:rokUplatneniVysledku
n4:2013
n3:tvurceVysledku
Uher, Václav Mašek, Jan Burget, Radim
n3:typAkce
n22:WRD
n3:zahajeniAkce
2013-09-13+02:00
s:numberOfPages
510
n13:doi
10.1007/978-3-642-41013-0_39
n23:hasPublisher
Springer-Verlag. (Berlin; Heidelberg)
n15:isbn
978-3-642-41012-3
n17:organizacniJednotka
26220