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.

In this article, we study the efficiency problem of video stream near-duplicate monitoring in a large-scale repository. Existing stream monitoring methods are mainly designed for a short video to scan over a query stream; they have difficulty being scalable for a large number of long videos. We present a simple but effective algorithm called incremental similarity update to address the problem. That is, a similarity upper bound between two videos can be calculated incrementally by leveraging the prior knowledge of the previous calculation. The similarity upper bound takes a lightweight computation to filter out unnecessary time-consuming computation for the actual similarity between two videos, making the search process more efficient. We integrate the algorithm with inverted indexing to obtain a candidate list from the repository for the given query stream. Meanwhile, the algorithm is applied to scan each candidate for locating exact near-duplicate subsequences. We implement several state-of-the-art methods for comparison in terms of accuracy, execution time, and memory consumption. Experimental results demonstrate the proposed algorithm yields comparable accuracy, compact memory size, and more efficient execution time.