A word-based approach for duplicate picture in picture sequence detection

A novel word-based algorithm is presented to detect duplicate Picture in Picture (PiP) video sequences in this study. The conventional edge-based methods used to extract the PiP regions are not robust in the noise and blurring images. Bag of Words (BOW) model emphasizes words ambiguity and ignores spatial information. Without detecting the PiP regions and unlike the traditional word based approach, the algorithm grasps the information of visual spatial transformation via exploring the attributes of local matching key-point pairs. The pairs are generated by directly comparing the visual words. Finally, the impact of the words representations is discussed thoroughly, such as words size, diversity and weighting. The experiment is conducted in the TRECVID 2010 content-based copy detection developing database and F-measure is up to 94%. From the results, the algorithm is effective and efficient for the PiP video sequence detection.