Tekrar eden sıralıların belirlenmesi ve takibi

In this thesis, we propose a new method to search dierent instances of a video sequence inside a long video. The proposed method is robust to view point and illumination changes which may occur since the sequences are captured in dierent times with dierent cameras, and to the dierences in the order and the number of frames in the sequences which may occur due to editing. The algorithm does not require any query to be given for searching, and nds all repeating video sequences inside a long video in a fully automatic way. First, the frames in a video are ranked according to their similarity on the distribution of salient points and colour values. Then, a tree based approach is used to seek for the repetitions of a video sequence if there is any. These repeating sequences are pruned for more accurate results in the last step. Results are provided on two full length feature movies, Run Lola Run and Groundhog Day, on commercials of TRECVID 2004 news video corpus and on dataset created for CIVR Copy Detection Showcase 2007. In these experiments, we obtain %93 precision values for CIVR2007 Copy Detection Showcase dataset and exceed %80 precision values for other sets. Keywords: copy detection, media tracking, story tracking.