Grid Labeling: Crowdsourcing Task-Specific Importance from Visualizations

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dc.contributor.authorChang, Minsuken_US
dc.contributor.authorWang, Yaoen_US
dc.contributor.authorWang, Huichen Willen_US
dc.contributor.authorBulling, Andreasen_US
dc.contributor.authorBearfield, Cindy Xiongen_US
dc.contributor.editorEl-Assady, Mennatallahen_US
dc.contributor.editorOttley, Alvittaen_US
dc.contributor.editorTominski, Christianen_US
dc.date.accessioned2025-05-26T06:59:10Z
dc.date.available2025-05-26T06:59:10Z
dc.date.issued2025
dc.description.abstractKnowing where people look in visualizations is key to effective design. Yet, existing research primarily focuses on free-viewingbased saliency models- although visual attention is inherently task-dependent. Collecting task-relevant importance data remains a resource-intensive challenge. To address this, we introduce Grid Labeling - a novel annotation method for collecting task-specific importance data to enhance saliency prediction models. Grid Labeling dynamically segments visualizations into Adaptive Grids, enabling efficient, low-effort annotation while adapting to visualization structure. We conducted a humansubject study comparing Grid Labeling with existing annotation methods, ImportAnnots, and BubbleView across multiple metrics. Results show that Grid Labeling produces the least noisy data and the highest inter-participant agreement with fewer participants while requiring less physical (e.g., clicks/mouse movements) and cognitive effort. An interactive demo and the accompanying dataset are available at https://github.com/jangsus1/Grid-Labeling.en_US
dc.description.sectionheadersTechniques and Tools
dc.description.seriesinformationEuroVis 2025 - Short Papers
dc.identifier.doi10.2312/evs.20251092
dc.identifier.isbn978-3-03868-282-0
dc.identifier.pages5 pages
dc.identifier.urihttps://doi.org/10.2312/evs.20251092
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/evs20251092
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing → Visualization techniques; Empirical studies in visualization
dc.subjectHuman centered computing → Visualization techniques
dc.subjectEmpirical studies in visualization
dc.titleGrid Labeling: Crowdsourcing Task-Specific Importance from Visualizationsen_US
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