Bernard, J., Dobermann, E., Bögl, M., Röhlig, M., Vögele, A., & Kohlhammer, J. (2016). Visual-Interactive Segmentation of Multivariate Time Series. In N. Andrienko & M. Sedlmair (Eds.), EuroVA 2016 EuroVis Workshop on Visual Analytics (pp. 31–35). The Eurographics Association. https://doi.org/10.2312/eurova.20161121
7th International Eurovis Workshop on Visual Analytics (EuroVA)
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Event date:
6-Jun-2016 - 7-Jun-2016
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Event place:
Groningen, the Netherlands, EU
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Number of Pages:
5
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Publisher:
The Eurographics Association
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Publisher:
The Eurographics Association
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Peer reviewed:
Yes
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Keywords:
Time series analysis; Time series segmentation; Classifier design and evaluation
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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.
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Project title:
CVAST: Centre for Visual Analytics Science and Technology (Laura Bassi Centre of Expertise) Visual Segmentation and Labeling of Multivariate Time Series: 2850-N31 (Fonds zur Förderung der wissenschaftlichen Forschung (FWF))
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Research Areas:
Visual Computing and Human-Centered Technology: 60% Logic and Computation: 40%
