Abstract 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. Graphic abstract

中文翻译：

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使用基于图像的聚类探索眼动数据

摘要 在本文中，我们描述了一种基于基于图像的聚类来探索眼动数据的新功能。为了达到这个目标，视觉注意力被考虑到从呈现的刺激中计算缩略图列表。这些缩略图携带有关视觉扫描策略的信息，但仅以空间填充和无序方式显示它们不支持对空间、时间或研究参与者的模式检测。在本文中，我们提出了 EyeCloud 方法的增强，该方法基于适用于图像缩略图的标准词云布局，通过利用图像信息对视觉上关注的缩略图进行聚类和分组。为了还表明缩略图的时间序列，我们添加了颜色编码的链接和进一步的视觉特征，以更深入地挖掘视觉注意力数据。该技术的实用性通过将其应用于先前进行的眼动追踪实验的眼动数据来说明，该实验调查了公共交通地图中的路线查找任务。最后，我们讨论了该方法的局限性和可扩展性问题。图形摘要