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17 February 2011 Non-supervised macro segmentation of large-scale TV videos
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Abstract
In this paper, a novel non-supervised macro segmentation algorithm is presented by detecting duplicate sequences of large-scale TV videos. Motivated by the fact that "Inter-Programs" are repeatedly inserted into the TV videos, the macro structure of the videos can be effectively and automatically generated by identifying the special sequences. There are four sections in the algorithm, namely, keyframe extraction, discrete cosine transformbased feature generation(a fixed-size 64D signature), Locality-Sensitive Hashing (LSH)-based frame retrieval and macro segmentation through the duplicated sequence detection and the dynamic programming. The main contributions are: (1) supply one effective and efficient algorithm for the macro segmentation in the large-scale TV videos, (2) LSH can quickly query the similar frames, and (3) the non-supervised learned duplicate sequence models are used to find the lost duplicate sequences by the dynamic programming. The algorithm has been tested in 15-day different-type TV streams. The F-measure of the system is greater than 96%. The experiments show that it is efficient and effective for the macro segmentation.
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Hongliang Bai, Chengyu Dong, Lezi Wang, Gang Qin, Kun Tao, Xiaofu Chang, and Yuan Dong "Non-supervised macro segmentation of large-scale TV videos", Proc. SPIE 7881, Multimedia on Mobile Devices 2011; and Multimedia Content Access: Algorithms and Systems V, 78811F (17 February 2011); https://doi.org/10.1117/12.872095
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