Redundant Coding Can Speed Up Segmentation in Multiclass Displays
Multiclass 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 easily
perceived 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 blue, or
triangular. One common technique is to combine these cues in a
redundant fashion, encoding one class as red and circular, and the
other as blue and triangular, under the assumption that a larger
difference (via multiple differing features) should help. Recent
work [6] has empirically demonstrated strengthened grouping and
improved accuracy in segmentation of redundantly coded objects.
Does this redundancy benefit generalize to more realistic displays,
and to other measures such as segmentation speed? We
demonstrate in an experiment that redundant coding can lead to a
small improvement in speed of visual differentiation in a
simulated dataset in a crowded display.
Images and movies
BibTex references
@InProceedings{NGF16, author = "Nothelfer, Christine and Gleicher, Michael and Franconeri, Steve", title = "Redundant Coding Can Speed Up Segmentation in Multiclass Displays", booktitle = "IEEE Visualization Poster Proceedings", month = "Oct", year = "2016", url = "http://graphics.cs.wisc.edu/Papers/2016/NGF16" }