Near-Duplicate Subsequence Matching Between the Continuous Stream and Large Video Dataset

In this paper, we study the subsequence matching problem of near-duplicate video detection. In particular, we address the application of monitoring a continuous stream with a large video dataset. To achieve real-time response and high accuracy, we propose a novel framework containing two characteristics . First, the subsequence matching is transformed to a 2-D Hough space projection of pairwise frame similarities between two subsequences. We present an approximate Hough transform that replaces the 2-D Hough space with a 1-D Hough space. The near-duplicate subsequence detection can be deemed to be the voting and searching in the 1-D Hough space with a lower time complexity. Second, a coarse-to-fine matching strategy is incorporated in the proposed framework. The coarse level matching selects the candidate videos based on the time-decay hit frequency between the query stream and dataset videos. The fine level matching applies the approximate Hough transform to detect the near-duplicate subsequences coexisting within the query stream and candidate videos. Several state-of-the-art methods are implemented for comparison. Experimental results show our framework outperforms in terms of accuracy and efficiency.