Dynamic label sampling on fisheye maps for information exploration

Abstract

For data with large dimensionality, placing labels is critical for users' comprehension of a scatterplot or a map of items. We propose a dynamic label sampling technique that, combined with graphical fisheye views, selects appropriate labels out of a large set of items on a map. Labels are sampled to give focus and contextual information according to users' panning/zooming and filtering operation. The paper also demonstrates an example of visual exploration with the image browser based on our technique.