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dc.contributor.authorWöhler, Leslieen_US
dc.contributor.authorZou, Yuxinen_US
dc.contributor.authorMühlhausen, Moritzen_US
dc.contributor.authorAlbuquerque, Georgiaen_US
dc.contributor.authorMagnor, Marcusen_US
dc.contributor.editorSchulz, Hans-Jörg and Teschner, Matthias and Wimmer, Michaelen_US
dc.description.abstractVisual quality metrics describe the quality and efficiency of multidimensional data visualizations in order to guide data analysts during exploration tasks. Current metrics are usually based on empirical algorithms which do not accurately represent human perception and therefore often differ from the analysts' expectations. We propose a new perception-based quality metric using deep learning that rates the correlation of data dimensions visualized by scatterplots. First, we created a data set containing over 15,000 pairs of scatterplots with human annotations on the perceived correlation between the data dimensions. Afterwards, we trained two different Convolutional Neural Networks (CNN), one extracts features from scatterplot images and the other directly from data vectors. We evaluated both CNNs on our test set and compared them to previous visual quality metrics. The experiments show that our new metric is able to represent human perception more accurately than previous methods.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectGeneral and reference
dc.subjectcentered computing
dc.subjectVisual analytics
dc.subjectComputing methodologies
dc.subjectNeural networks
dc.titleLearning a Perceptual Quality Metric for Correlation in Scatterplotsen_US
dc.description.seriesinformationVision, Modeling and Visualization
dc.description.sectionheadersMachine Learning in Vision and Analysis

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  • VMV19
    ISBN 978-3-03868-098-7

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