Fast and Accurate CNN-based Brushing in Scatterplots

dc.contributor.authorFan, Chaoranen_US
dc.contributor.authorHauser, Helwigen_US
dc.contributor.editorJeffrey Heer and Heike Leitte and Timo Ropinskien_US
dc.date.accessioned2018-06-02T18:07:25Z
dc.date.available2018-06-02T18:07:25Z
dc.date.issued2018
dc.description.abstractBrushing plays a central role in most modern visual analytics solutions and effective and efficient techniques for data selection are key to establishing a successful human-computer dialogue. With this paper, we address the need for brushing techniques that are both fast, enabling a fluid interaction in visual data exploration and analysis, and also accurate, i.e., enabling the user to effectively select specific data subsets, even when their geometric delimination is non-trivial. We present a new solution for a near-perfect sketch-based brushing technique, where we exploit a convolutional neural network (CNN) for estimating the intended data selection from a fast and simple click-and-drag interaction and from the data distribution in the visualization. Our key contributions include a drastically reduced error rate-now below 3%, i.e., less than half of the so far best accuracy- and an extension to a larger variety of selected data subsets, going beyond previous limitations due to linear estimation models.en_US
dc.description.number3
dc.description.sectionheadersHigh-dimensional Data
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume37
dc.identifier.doi10.1111/cgf.13405
dc.identifier.issn1467-8659
dc.identifier.pages111-120
dc.identifier.urihttps://doi.org/10.1111/cgf.13405
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13405
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.titleFast and Accurate CNN-based Brushing in Scatterplotsen_US
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