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Redundant Coding Can Improve Segmentation in Multiclass Displays

Christine Nothelfer, Michael Gleicher, Steve Franconeri
IEEE Visualization Poster Proceedings — Oct 2015
    Download the publication : ngf15.pdf [357Ko]  
    Data visualizations allow viewers to compare one dataset to another. The visual marks that represent these datasets, or classes, are visually distinguished from one another by salient visual feature differences, such as color or shape. A designer of a graph or map might encode one class of marks as either red, or circular, and another class as either green, or square. One common technique is to combine these cues in a redundant fashion, encoding one class as red and circular, and the other as green and square, under the assumption that a larger difference (via multiple differing features) should help. Despite the ubiquity of this practice, we know of no empirical demonstration that reveals evidence of a potential benefit. Across two experiments, we demonstrate that redundant coding can improve visual segmentation of a simulated dataset in a crowded display (Experiment 1) and that redundant coding also leads to stronger visual grouping of elements (Experiment 2).

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    BibTex references

      author       = "Nothelfer, Christine and Gleicher, Michael and Franconeri, Steve",
      title        = "Redundant Coding Can Improve Segmentation in Multiclass Displays",
      booktitle    = "IEEE Visualization Poster Proceedings",
      month        = "Oct",
      year         = "2015",
      url          = "http://graphics.cs.wisc.edu/Papers/2015/NGF15"

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