Fast and robust detection of near-duplicates in web video database

Web video databases tend to contain tremendous near-duplicates with the explosive growth of online videos. How to detect and eliminate these near-duplicates has become an essential problem for Web video storage and indexing. In this paper, we propose a hierarchical approach to solve this problem efficiently and effectively. For an incoming video, firstly we compare global features to exclude videos with large differences. Then pairwise comparison of key-frame level features with more computation cost is performed. For pairwise comparison, we adopt the maximum matching (MM) framework on bipartite graph. Instead of using standard well-known MM algorithm, a simple approximate algorithm of MM and a graph cut algorithm are applied for a higher speed. Experimental results over two databases summing up to more than 20,000 videos demonstrate our algorithmpsilas excellent performance in both speed and accuracy.