Robust video hashing based on representative-dispersive frames

• Published in: Science China. Information sciences Vol. 56; no. 6; pp. 212 - 222
• Main Authors: Nie, XiuShan, Liu, Ju, Sun, JianDe, Wang, LianQi, Yang, XiaoHui
• Format: Journal Article
• Language: English
• Published: Heidelberg SP Science China Press 01.06.2013; Springer Nature B.V
• Subjects: Apexes; Channels; Computer Science; Dispersion; Frames; Frames (data processing); Graph theory; Graphical representations; Graphs; Impairment; Information Systems and Communication Service; Mathematical analysis; Reproduction; Research Paper; Robustness; Similarity; Spatial data; 四阶累计量; 散列; 断层图像; 空间信息; 色散; 视频帧; 视频拷贝; 顶点表示; representative-dispersive frames; video hashing; video copy detection; video tomography
• ISSN: 1674-733X; 1869-1919
• DOI: 10.1007/s11432-012-4760-y

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.