Clutter-aware label layout

Abstract

A high-quality label layout is critical for effective information understanding and consumption. Existing labeling methods fail to help users quickly gain an overview of visualized data when the number of labels is large. Visual clutter is a major challenge preventing these methods from being applied to real-world applications. To address this, we propose a context-aware label layout that can measure and reduce visual clutter during the layout process. Our method formulates the clutter model using four factors: confusion, visual connection, distance, and intersection. Based on this clutter model, an effective clutter-aware labeling method has been developed that can generate clear and legible label layouts in different visualizations. We have applied our method to several types of visualizations and the results show promise, especially in support of an uncluttered and informative label layout.