Detail publikace

IMMI: Interactive Segmentation Toolkit

Originální název

IMMI: Interactive Segmentation Toolkit

Anglický název

IMMI: Interactive Segmentation Toolkit

Jazyk

en

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.

Anglický 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.

BibTex


@inproceedings{BUT101149,
  author="Jan {Mašek} and Radim {Burget} and Václav {Uher}",
  title="IMMI: Interactive Segmentation Toolkit",
  annote="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.",
  address="Springer Berlin Heidelberg",
  booktitle="Engineering Applications of Neural Networks",
  chapter="101149",
  doi="10.1007/978-3-642-41013-0_39",
  howpublished="online",
  institution="Springer Berlin Heidelberg",
  number="24",
  year="2013",
  month="september",
  pages="380--387",
  publisher="Springer Berlin Heidelberg",
  type="conference paper"
}