Fast and Accurate CNN-based Brushing in Scatterplots
| dc.contributor.author | Fan, Chaoran | en_US |
| dc.contributor.author | Hauser, Helwig | en_US |
| dc.contributor.editor | Jeffrey Heer and Heike Leitte and Timo Ropinski | en_US |
| dc.date.accessioned | 2018-06-02T18:07:25Z | |
| dc.date.available | 2018-06-02T18:07:25Z | |
| dc.date.issued | 2018 | |
| dc.description.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. | en_US |
| dc.description.number | 3 | |
| dc.description.sectionheaders | High-dimensional Data | |
| dc.description.seriesinformation | Computer Graphics Forum | |
| dc.description.volume | 37 | |
| dc.identifier.doi | 10.1111/cgf.13405 | |
| dc.identifier.issn | 1467-8659 | |
| dc.identifier.pages | 111-120 | |
| dc.identifier.uri | https://doi.org/10.1111/cgf.13405 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.1111/cgf13405 | |
| dc.publisher | The Eurographics Association and John Wiley & Sons Ltd. | en_US |
| dc.title | Fast and Accurate CNN-based Brushing in Scatterplots | en_US |