IBM Research TRECVID-2010 Video Copy Detection and Multimedia Event Detection System
Matthew L. Hill, Gang Hua, Apostol Natsev, John R. Smith, Lexing Xie, Bert Huang, Michele Merler, Hua Ouyang, Mingyuan Zhou · 2010
In this paper, we describe the system jointly developed by IBM Research and Columbia University for video copy detection and multimedia event detection applied to the TRECVID-2010 video retrieval benchmark. A. Content-Based Copy Detection: The focus of our copy detection system this year was fusing three types of complementary fingerprints: a keyframe-based color correlogram, SIFTogram (bag of visual words), and a GIST-based fingerprint. However, in our official submissions, we did not use the color correlogram component since our best results on the training set came from the GIST and SIFTogram components. A summary of our runs is listed below: 1. IBM.m.nofa.gistG: A run based on the grayscale GIST frame-level feature, with at most 1 result per query, except in the case of ties. 2. IBM.m.balanced.gistG: As in the above run, but with including more results per query, though on average still less than 2. 3. IBM.m.nofa.gistGC: The result of the nofa.gistG run, fused with results from GIST features extracted from the R,G,B color channels. 4. IBM.m.nofa.gistGCsift: The result of the nofa.giestGC run, fused with a SIFTogram result.