Fast Video Copy Detection
| With digital video content production and distribution continuing to grow, content-based
copy detection (CBCD) has been actively studied for a wide range of
applications that include searching, multimedia linking, and protecting copyrighted
content. Based on content alone, CBCD attempts to identify segments in a query video that are copies
from a reference video database. A copy is not an exact duplicate but,
in general, either a transformed or a modified version of the original
document that remains recognizable. This task is challenging since two copies
might be visiaully dissimilar, as shown in the figure below.
We propose an edit-distance-based approach that has the potential for large-scale CBCD applications. We first formulate a local alignment problem between two sequences and extend previous edit-distance-based approaches to compare video segments of all possible lengths. The main contribution of this work is the highly efficient matching process between a query video and a video database which is achieved by a fast local alignment method along with a dedicated index structure that provides detection acceleration at both the clip and frame levels. We demonstrate the effectiveness and efficiency of this method using the MUSCLE VCD benchmark.
Yeh and Kwang-Ting Cheng. Fast Visual Retrieval Using Accelerated Sequence
Matching, to appear in IEEE Transactions on Multimedia.
Mei-Chen Yeh and Kwang-Ting Cheng. Video Copy Detection by Fast Sequence Matching. ACM International Conference on Image and Video Retrieval 2009 (ACM CIVR 09), July 8-10, Island of Santorini, Greece.
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