  An effective approach to automated movie content analysis involves building a
network (graph) of its characters. Existing work usually builds a static
character graph to summarize the content using metadata, scripts or manual
annotations. We propose an unsupervised approach to building a dynamic
character graph that captures the temporal evolution of character interaction.
We refer to this as the character interaction graph(CIG). Our approach has two
components:(i) an online face clustering algorithm that discovers the
characters in the video stream as they appear, and (ii) simultaneous creation
of a CIG using the temporal dynamics of the resulting clusters. We demonstrate
the usefulness of the CIG for two movie analysis tasks: narrative structure
(acts) segmentation, and major character retrieval. Our evaluation on
full-length movies containing more than 5000 face tracks shows that the
proposed approach achieves superior performance for both the tasks.
