Grid Labeling: Crowdsourcing Task-Specific Importance from Visualizations

TL;DR

This paper introduces Grid Labeling, a new annotation method for efficiently collecting task-specific importance data in visualizations, improving saliency prediction models with less effort and noise.

Contribution

The paper presents Grid Labeling, a novel adaptive segmentation technique that enhances data collection for visualization saliency by reducing effort and noise compared to existing methods.

Findings

Grid Labeling produces less noisy importance data.

It achieves higher inter-participant agreement with fewer participants.

Requires less physical and cognitive effort than existing methods.

Abstract

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.