Visual-Interactive Segmentation of Multivariate Time Series
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 dat
Bilder
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Bibtex
@INPROCEEDINGS{Bernard-2016-Visual, author = {Bernard, J{\"u}rgen and Dobermann, Eduard and B{\"o}gl, Markus and R{\"o}hlig, Martin and V{\"o}gele, Anna and Kohlhammer, J{\"o}rn}, title = {Visual-Interactive Segmentation of Multivariate Time Series}, booktitle = {EuroVis Workshop on Visual Analytics (EuroVA)}, year = {2016}, month = jun, publisher = {The Eurographics Association}, location = {Groningen, Nederlands}, 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 dat}, isbn = {978-3-03868-016-1}, doi = {10.2312/eurova.20161121} }