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Visual-interactive segmentation of multivariate time series

 
: Bernard, Jürgen; Dobermann, Eduard; Bögl, Markus; Röhlig, Martin; Vögele, Anna; Kohlhammer, Jörn

:

Andrienko, Natalia (Ed.); Sedlmaier, Michael (Ed.) ; European Association for Computer Graphics -EUROGRAPHICS-:
EuroVis Workshop on Visual Analytics, EuroVA 2016 : Groningen, the Netherlands, 6-10 June 2016
Aire-la-Ville: Eurographics Association, 2016
ISBN: 978-3-03868-016-1
S.31-35
International Workshop on Visual Analytics (EuroVA) <7, 2016, Groningen>
Eurographics Conference on Visualization (EuroVis) <18, 2016, Groningen>
Englisch
Konferenzbeitrag
Fraunhofer IGD ()
Fraunhofer IGD-R ()
information visualization; Visual analytics; time series analysis; data mining; machine learning; clustering; human motion analysis; Guiding Theme: Digitized Work; Guiding Theme: Individual Health; Guiding Theme: Smart City; Research Area: Computer graphics (CG); Research Area: Computer vision (CV); Research Area: Human computer interaction (HCI)

Abstract
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.

: http://publica.fraunhofer.de/dokumente/N-461502.html