Content-based video copy detection using nearest-neighbor mapping

We report results on video copy detection using nearest-neighbor (NN) mapping that has been used successfully in audio copy detection. For copy detection search, we use a sliding window to move the query video over the test video, and count the number of frames of query that match the frames in the test segment. The feature in the test frame that we match is the frame number of the query that is closest to that test frame. This leads to good matching scores even when the query video is distorted and contains occlusions. We test the NN mapping algorithm and the video features that map test frame to the closest query frame on TRECVID 2009 and 2010 content-based copy detection (CBCD) evaluation data. For both these tasks, the NN mapping for video copy detection gives minimal normalized detection cost rate (min NDCR) comparable to that achieved with audio copy detection for the same task. For the TRECVID 2011 CBCD evaluation data we got the lowest min NDCR for 26 out of 56 transforms for actual no false alarm case.