Copy detection towards semantic mining for video retrieval
In large-scale video database, lots of different videos frequently share the similar content copied from the same source. Generally, those videos have certain semantic correlations, such as being of similar events and sharing the same topic. Mining these semantic correlations can greatly facilitate video search. However, as a preprocessing step, detecting and localizing the copy pair among videos, i.e. copy detection problem, plays a key role for precise semantic mining. To meet the requirements in semantic mining scenario, we propose a frame fusion based copy detection scheme. In this scheme, the copy detection problem is converted to HMM decoding problem with three relaxed constraints, where Viterbi algorithm is employed to automatically detect the copy pair. The experimental results show that the proposed approach achieves high localization accuracy even when the copied clip undergoes some complex transformations, while achieving comparable performance compared with state-of-the-art copy detection methods.