Visual-Interactive Segmentation of Multivariate Time Series

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}
}