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Learning a Perceptual Quality Metric for Correlation in Scatterplots

Wöhler, Leslie and Zou, Yuxin and Mühlhausen, Moritz and Albuquerque, Georgia and Magnor, Marcus (2019) Learning a Perceptual Quality Metric for Correlation in Scatterplots. In: VMV 2019 - Vision, Modeling and Visualization. The Eurographics Association. Vision, Modeling and Visualization (VMV), 2019-09-30 - 2019-10-02, Rostock, Germany. doi: 10.2312/vmv.20191318. ISBN 978-3-03868-098-7.

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Official URL: https://diglib.eg.org/xmlui/handle/10.2312/2632811


Visual 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.

Item URL in elib:https://elib.dlr.de/132277/
Document Type:Conference or Workshop Item (Speech)
Title:Learning a Perceptual Quality Metric for Correlation in Scatterplots
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Wöhler, LeslieComputer Graphics Lab, TU Braunschweig, GermanyUNSPECIFIEDUNSPECIFIED
Zou, YuxinComputer Graphics Lab, TU Braunschweig, GermanyUNSPECIFIEDUNSPECIFIED
Mühlhausen, MoritzComputer Graphics Lab, TU Braunschweig, GermanyUNSPECIFIEDUNSPECIFIED
Albuquerque, GeorgiaUNSPECIFIEDhttps://orcid.org/0000-0002-4510-3383UNSPECIFIED
Magnor, MarcusComputer Graphics Lab, TU Braunschweig, GermanyUNSPECIFIEDUNSPECIFIED
Date:30 September 2019
Journal or Publication Title:VMV 2019 - Vision, Modeling and Visualization
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
EditorsEmailEditor's ORCID iDORCID Put Code
Publisher:The Eurographics Association
Keywords:visual analytics, visual quality metrics, deep learning, maschine learning
Event Title:Vision, Modeling and Visualization (VMV)
Event Location:Rostock, Germany
Event Type:international Conference
Event Start Date:30 September 2019
Event End Date:2 October 2019
Organizer:University of Rostock, , Germany
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:other
DLR - Research area:Raumfahrt
DLR - Program:R - no assignment
DLR - Research theme (Project):R - no assignment
Location: Braunschweig
Institutes and Institutions:Institut of Simulation and Software Technology > Software for Space Systems and Interactive Visualisation
Deposited By: Albuquerque, Dr.-Ing. Georgia
Deposited On:18 Dec 2019 13:17
Last Modified:24 Apr 2024 20:35

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