EuroVA16

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Visual-Interactive Segmentation of Multivariate Time Series
(The Eurographics Association, 2016) Bernard, Jürgen; Dobermann, Eduard; Bögl, Markus; Röhlig, Martin; Vögele, Anna; Kohlhammer, Jörn; Natalia Andrienko and Michael Sedlmair
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.