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MAŠEK, J. BURGET, R. UHER, V.
Originální název
IMMI: Interactive Segmentation Toolkit
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
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.
Klíčová slova
Classification, image segmentation, interactive tool, IMMI, RapidMiner
Autoři
MAŠEK, J.; BURGET, R.; UHER, V.
Rok RIV
2013
Vydáno
15. 9. 2013
Nakladatel
Springer Berlin Heidelberg
Místo
Heidelberg
ISBN
978-3-642-41012-3
Kniha
Engineering Applications of Neural Networks
ISSN
1865-0929
Periodikum
Communications in Computer and Information Science
Ročník
383
Číslo
24
Stát
Spolková republika Německo
Strany od
380
Strany do
387
Strany počet
510
BibTex
@inproceedings{BUT101149, author="Jan {Mašek} and Radim {Burget} and Václav {Uher}", title="IMMI: Interactive Segmentation Toolkit", booktitle="Engineering Applications of Neural Networks", year="2013", journal="Communications in Computer and Information Science", volume="383", number="24", pages="380--387", publisher="Springer Berlin Heidelberg", address="Heidelberg", doi="10.1007/978-3-642-41013-0\{_}39", isbn="978-3-642-41012-3", issn="1865-0929" }