FaceCloud: Heterogeneous Cloud Visualization of Multiplex Networks for Multimedia Archive Exploration

Multimedia data is by nature heterogeneous, conveying semantic information through multiple cues. Text analysis of closed captions already brought us understanding of the spoken information. Today's advances in computer vision now enable us to look for relevant semantic information from the visual content of real-world archives. Combining these two levels of extracted information to make sense of an archive still remains a challenge. Multiplex net- works, which model multiple families of interactions in a graph, can capture and combine both sources of semantics. We can leverage on these objects to extract hierarchies and integrate them in an interactive heterogeneous "visual cloud". Inspired by word clouds, these clouds allow to grasp visual and textual semantic information captured from a multimedia collection all at once. The interaction then enables direct access to the relevant video. We demonstrate our system with the exploration of a Japanese news archive.