Color and Shape efficiency for outlier detection from automated to user evaluation

• Published in: Visual informatics (Online) Vol. 6; no. 2; pp. 25 - 40
• Main Authors: Giovannangeli, Loann, Bourqui, Romain, Giot, Romain, Auber, David
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2022; Elsevier
• Subjects: Artificial Intelligence; Automated evaluation; Computer Science; Computer Vision and Pattern Recognition; Deep learning; Machine Learning; Outlier detection; User evaluation; Visual search; Deep learning; Outlier detection; Automated evaluation; User evaluation; Visual search
• DOI: 10.1016/j.visinf.2022.03.001
• ISSN: 2468-502X; 2543-2656

The design of efficient representations is well established as a fruitful way to explore and analyze complex or large data. In these representations, data are encoded with various visual attributes depending on the needs of the representation itself. To make coherent design choices about visual attributes, the visual search field proposes guidelines based on the human brain’s perception of features. However, information visualization representations frequently need to depict more data than the amount these guidelines have been validated on. Since, the information visualization community has extended these guidelines to a wider parameter space. This paper contributes to this theme by extending visual search theories to an information visualization context. We consider a visual search task where subjects are asked to find an unknown outlier in a grid of randomly laid out distractors. Stimuli are defined by color and shape features for the purpose of visually encoding categorical data. The experimental protocol is made of a parameters space reduction step (i.e., sub-sampling) based on a machine learning model, and a user evaluation to validate hypotheses and measure capacity limits. The results show that the major difficulty factor is the number of visual attributes that are used to encode the outlier. When redundantly encoded, the display heterogeneity has no effect on the task. When encoded with one attribute, the difficulty depends on that attribute heterogeneity until its capacity limit (7 for color, 5 for shape) is reached. Finally, when encoded with two attributes simultaneously, performances drop drastically even with minor heterogeneity.