Visual quality metrics and human perception: An initial study on 2D projections of large multidimensional data

Andrada Tatu, Peter Bak, Enrico Bertini, Daniel Keim, Joern Schneidewind

    Research output: Chapter in Book/Report/Conference proceedingConference contribution


    Visual quality metrics have been recently devised to automatically extract interesting visual projections out of a large number of available candidates in the exploration of highdimensional databases. The metrics permit for instance to search within a large set of scatter plots (e.g., in a scatter plot matrix) and select the views that contain the best separation among clusters. The rationale behind these techniques is that automatic selection of "best" views is not only useful but also necessary when the number of potential projections exceeds the limit of human interpretation. While useful as a concept in general, such metrics received so far limited validation in terms of human perception. In this paper we present a perceptual study investigating the relationship between human interpretation of clusters in 2D scatter plots and the measures automatically extracted out of them. Specifically we compare a series of selected metrics and analyze how they predict human detection of clusters. A thorough discussion of results follows with reflections on their impact and directions for future research.

    Original languageEnglish (US)
    Title of host publicationProceedings of the Working Conference on Advanced Visual Interfaces, AVI' 10
    Number of pages8
    StatePublished - 2010
    EventInternational Conference on Advanced Visual Interfaces, AVI '10 - Rome, Italy
    Duration: May 26 2010May 28 2010

    Publication series

    NameProceedings of the Workshop on Advanced Visual Interfaces AVI


    OtherInternational Conference on Advanced Visual Interfaces, AVI '10


    • User study
    • Visual quality metrics

    ASJC Scopus subject areas

    • Software
    • Human-Computer Interaction

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