Near-Duplicate Subsequence Matching for Video Streams

In this paper, we study the efficiency problem of near-duplicate subsequence matching for video streams. A simple but effective algorithm called incremental similarity update is proposed to address the problem. A similarity upper bound between two videos can be calculated incrementally by taking a lightweight computation to filter out the unnecessary time-consuming computation for the actual similarity between two videos. We integrate the algorithm with inverted frame indexing to scan video sequences for matching near-duplicate subsequences. Four state-of-the-art methods are implemented for comparison in terms of the accuracy, execution time, and memory consumption. Experimental results demonstrate the proposed algorithm yields comparable accuracy, compact memory size, and more efficient execution time.