Robust video hashing based on representative-dispersive frames |
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Authors: | XiuShan Nie Ju Liu JianDe Sun LianQi Wang XiaoHui Yang |
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Affiliation: | 1. School of Information Science and Technology, Shandong University of Finance and Economics, Jinan, 250014, China 2. School of Information Science and Engineering, Shandong University, Jinan, 250100, China 3. Hisense State Key Laboratory of Digital Multi-Media Technology, Qingdao, 266071, China 4. Department of Physics and Astronomy, University of California, Irvine, Irvine, CA, 92697, USA
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Abstract: | This study proposes a robust video hashing for video copy detection. The proposed method, which is based on representative-dispersive frames (R-D frames), can reveal the global and local information of a video. In this method, a video is represented as a graph with frames as vertices. A similarity measure is proposed to calculate the weights between edges. To select R-D frames, the adjacency matrix of the generated graph is constructed, and the adjacency number of each vertex is calculated, and then some vertices that represent the R-D frames of the video are selected. To reveal the temporal and spatial information of the video, all R-D frames are scanned to constitute an image called video tomography image, the fourth-order cumulant of which is calculated to generate a hash sequence that can inherently describe the corresponding video. Experimental results show that the proposed video hashing is resistant to geometric attacks on frames and channel impairments on transmission. |
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Keywords: | representative-dispersive frames video hashing video tomography video copy detection |
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