Fast and Accurate CNN-based Brushing in Scatterplots
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Date
2018
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Publisher
The Eurographics Association and John Wiley & Sons Ltd.
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.
Description
@article{10.1111:cgf.13405,
journal = {Computer Graphics Forum},
title = {{Fast and Accurate CNN-based Brushing in Scatterplots}},
author = {Fan, Chaoran and Hauser, Helwig},
year = {2018},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.13405}
}