Columbia University's semantic video search engine

Authors:
Shih-Fu Chang;Lyndon S. Kennedy;Eric Zavesky
Affiliations:
Columbia University, New York, NY;Columbia University, New York, NY;Columbia University, New York, NY
Venue:
Proceedings of the 6th ACM international conference on Image and video retrieval
Year:
2007

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Abstract

We briefly describe "CuVid," Columbia University's video search engine, a system that enables semantic multimodal search over video broadcast news collections. The system was developed and first evaluated for the NIST TRECVID 2005 benchmark and later expanded to include a large number (374) of visual concept detectors. Our focus is on comparative studies of pros and cons of search methods built on various individual modalities (keyword, image, near-duplicate, and semantic concept) and combinations, without requiring advanced tools and interfaces for interactive search.