Grid Labeling: Crowdsourcing Task-Specific Importance from Visualizations
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915329878261760 |
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| author | Chang, Minsuk Wang, Yao Wang, Huichen Will Bulling, Andreas Bearfield, Cindy Xiong |
| author_facet | Chang, Minsuk Wang, Yao Wang, Huichen Will Bulling, Andreas Bearfield, Cindy Xiong |
| contents | Knowing where people look in visualizations is key to effective design. Yet, existing research primarily focuses on free-viewing-based 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 human subject 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 is available at https://jangsus1.github.io/Grid-Labeling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_13902 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Grid Labeling: Crowdsourcing Task-Specific Importance from Visualizations Chang, Minsuk Wang, Yao Wang, Huichen Will Bulling, Andreas Bearfield, Cindy Xiong Human-Computer Interaction Knowing where people look in visualizations is key to effective design. Yet, existing research primarily focuses on free-viewing-based 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 human subject 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 is available at https://jangsus1.github.io/Grid-Labeling. |
| title | Grid Labeling: Crowdsourcing Task-Specific Importance from Visualizations |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2502.13902 |