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Multi-Modal Person-Profiles from Broadcast News Video

The need to analyze and index large amounts of video information is becoming more important as the way people consume media continues to change. In recent years, the push to attack multimedia indexing and retrieval applications in a holistic, multi-modal way has garnered great attention. In this work we propose the holistic use of both audio, visual and textual information for the automatic indexing of broadcast news video to create person-profiles. Indexing videos in this matter helps facilitate a unique way to create multimedia person databases automatically, as well as for attacking existing video analysis tasks. We test our algorithm on news data from NBC and present areas for future exploration.