Visual-Interactive Segmentation of Multivariate Time Series
Date
2016Author
Bernard, Jürgen
Dobermann, Eduard
Bögl, Markus
Röhlig, Martin
Vögele, Anna
Kohlhammer, Jörn
Metadata
Show full item recordAbstract
Choosing appropriate time series segmentation algorithms and relevant parameter values is a challenging problem. In order to choose meaningful candidates it is important that different segmentation results are comparable. We propose a Visual Analytics (VA) approach to address these challenges in the scope of human motion capture data, a special type of multivariate time series data. In our prototype, users can interactively select from a rich set of segmentation algorithm candidates. In an overview visualization, the results of these segmentations can be compared and adjusted with regard to visualizations of raw data. A similarity-preserving colormap further facilitates visual comparison and labeling of segments. We present our prototype and demonstrate how it can ease the choice of winning candidates from a set of results for the segmentation of human motion capture data.
BibTeX
@inproceedings {eurova.20161121,
booktitle = {EuroVis Workshop on Visual Analytics (EuroVA)},
editor = {Natalia Andrienko and Michael Sedlmair},
title = {{Visual-Interactive Segmentation of Multivariate Time Series}},
author = {Bernard, Jürgen and Dobermann, Eduard and Bögl, Markus and Röhlig, Martin and Vögele, Anna and Kohlhammer, Jörn},
year = {2016},
publisher = {The Eurographics Association},
ISSN = {-},
ISBN = {978-3-03868-016-1},
DOI = {10.2312/eurova.20161121}
}
booktitle = {EuroVis Workshop on Visual Analytics (EuroVA)},
editor = {Natalia Andrienko and Michael Sedlmair},
title = {{Visual-Interactive Segmentation of Multivariate Time Series}},
author = {Bernard, Jürgen and Dobermann, Eduard and Bögl, Markus and Röhlig, Martin and Vögele, Anna and Kohlhammer, Jörn},
year = {2016},
publisher = {The Eurographics Association},
ISSN = {-},
ISBN = {978-3-03868-016-1},
DOI = {10.2312/eurova.20161121}
}