Exploring eye movement data with image-based clustering

In this article, we describe a new feature for exploring eye movement data based on image-based clustering. To reach this goal, visual attention is taken into account to compute a list of thumbnail images from the presented stimulus. These thumbnails carry information about visual scanning strategies, but showing them just in a space-filling and unordered fashion does not support the detection of patterns over space, time, or study participants. In this article, we present an enhancement of the EyeCloud approach that is based on standard word cloud layouts adapted to image thumbnails by exploiting image information to cluster and group the thumbnails that are visually attended. To also indicate the temporal sequence of the thumbnails, we add color-coded links and further visual features to dig deeper in the visual attention data. The usefulness of the technique is illustrated by applying it to eye movement data from a formerly conducted eye tracking experiment investigating route finding tasks in public transport maps. Finally, we discuss limitations and scalability issues of the approach.