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- Title
Fast and Accurate CNN‐based Brushing in Scatterplots.
- Authors
Fan, Chaoran; Hauser, Helwig
- Abstract
Abstract: Brushing 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.
- Subjects
VISUAL analytics; SCATTER diagrams; HUMAN-computer interaction; DATA visualization; ARTIFICIAL neural networks
- Publication
Computer Graphics Forum, 2018, Vol 37, Issue 3, p111
- ISSN
0167-7055
- Publication type
Article
- DOI
10.1111/cgf.13405