Paper Info
Title
Saliency Deficit and Motion Outlier Detection in Animated Scatterplots.
Abstract
We report the results of a crowdsourced experiment that measured the accuracy of motion outlier detection in multivariate, animated scatterplots. The targets were outliers either in speed or direction of motion, and were presented with varying levels of saliency in dimensions that are irrelevant to the task of motion outlier detection (e.g., color, size, position). We found that participants had trouble finding the outlier when it lacked irrelevant salient features and that visual channels contribute unevenly to the odds of an outlier being correctly detected. Direction of motion contributes the most to accurate detection of speed outliers, and position contributes the most to accurate detection of direction outliers. We introduce the concept of saliency deficit in which item importance in the data space is not reflected in the visualization due to a lack of saliency. We conclude that motion outlier detection is not well supported in multivariate animated scatterplots.
Year
DOI
Venue
2019
10.1145/3290605.3300771
CHI
Keywords
Field
DocType
perception, saliency, visualization
Anomaly detection,Pattern recognition,Visualization,Salience (neuroscience),Multivariate statistics,Computer science,Outlier,Human–computer interaction,Artificial intelligence,Odds,Perception,Salient
Conference
ISBN
Citations 
PageRank 
978-1-4503-5970-2
4
0.38
References 
Authors
0
2
Name
Order
Citations
PageRank
Rafael Veras1815.18
Christopher Collins2103749.74