dc.contributor.author | Jänicke, Heike | en_US |
dc.contributor.author | Chen, Min | en_US |
dc.contributor.editor | G. Melancon, T. Munzner, and D. Weiskopf | en_US |
dc.date.accessioned | 2014-02-21T20:06:23Z | |
dc.date.available | 2014-02-21T20:06:23Z | |
dc.date.issued | 2010 | en_US |
dc.identifier.issn | 1467-8659 | en_US |
dc.identifier.uri | http://dx.doi.org/10.1111/j.1467-8659.2009.01667.x | en_US |
dc.description.abstract | Salience detection is a principle mechanism to facilitate visual attention. A good visualization guides the observer s attention to the relevant aspects of the representation. Hence, the distribution of salience over a visualization image is an essential measure of the quality of the visualization. We describe a method for computing such a metric for a visualization image in the context of a given dataset. We show how this technique can be used to analyze a visualization s salience, improve an existing visualization, and choose the best representation from a set of alternatives. The usefulness of this proposed metric is illustrated using examples from information visualization, volume visualization and flow visualization. | en_US |
dc.publisher | The Eurographics Association and Blackwell Publishing Ltd. | en_US |
dc.title | A Salience-based Quality Metric for Visualization | en_US |
dc.description.seriesinformation | Computer Graphics Forum | en_US |
dc.description.volume | 29 | en_US |
dc.description.number | 3 | en_US |