Visual-Interactive Segmentation of Multivariate Time Series

Jürgen Bernard, Eduard Dobermann, Markus Bögl, Martin Röhlig, Anna Vögele und Jörn Kohlhammer
In proceedings of EuroVis Workshop on Visual Analytics (EuroVA), Groningen, Nederlands, The Eurographics Association, Juni 2016
 

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

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}
}