MediaGLOW is an interactive visual workspace designed to address the growing number and size of digital photo libraries. It uses attributes such as visual appearance, GPS locations, user-assigned tags, and dates to filter and group photos. An automatic layout algorithm positions photos with similar attributes near each other to allow users to serendipitously find multiple relevant photos.

To let users make use of similarity layouts, we created an interactive visual workspace called MediaGLOW that presents a photo collection based on different similarity criteria. We currently offer four different similarity criteria: temporal, geographic, tag, visual. Temporal similarity is computed from the difference between photo creation times. Geographic similarity is based on the distance between latitude-longitude pairs. Tag similarity is computed using the Jaccard similarity coefficient of tags shared across photos. Our visual similarity is determined by an image classifier trained on manually tagged photos that compares predicted likelihoods for tags. In addition to grouping photos by similarity, MediaGLOW also provides three filters that restrict the time range, the geographic location, and the tags assigned to matching photos.

MediaGLOW integrates a variety of visualization and interaction techniques with different similarity criteria, enabling users to find relevant photos by proximity and by attribute filters. For placing photos in the 2D workspace, we chose a graph layout mechanism that visually indicates similarity among photos in the space while optimizing desired distances between photos. While grid-based layouts are more common for photo applications, they cannot accurately present similarity by proximity. Furthermore, while some similarity criteria, such as time, may naturally be visualized in one dimension, multi-dimensional similarity criteria can be visualized better in a two-dimensional layout.

Technical Contact: Andreas Girgensohn

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