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

Dynamic Character Graph via Online Face Clustering for Movie Analysis