计算机科学 ›› 2018, Vol. 45 ›› Issue (2): 84-89.doi: 10.11896/j.issn.1002-137X.2018.02.014

• 2017年中国计算机学会人工智能会议 • 上一篇    下一篇

基于短空时变化的鲁棒视频哈希算法

于晓,聂秀山,马林元,尹义龙   

  1. 山东财经大学计算机科学与技术学院 济南250014,山东财经大学计算机科学与技术学院 济南250014;山东大学计算机科学与技术学院 济南250100,山东财经大学实验教学中心 济南250014,山东财经大学计算机科学与技术学院 济南250014;山东大学计算机科学与技术学院 济南250100
  • 出版日期:2018-02-15 发布日期:2018-11-13
  • 基金资助:
    本文受国家自然基金项目(61671274),中国博士后科学基金项目(2016M592190),山东省高等学校科技计划项目(J17KB161),山东省高等学校优势学科人才团队培育计划资助

Robust Video Hashing Algorithm Based on Short-term Spatial Variations

YU Xiao, NIE Xiu-shan, MA Lin-yuan and YIN Yi-long   

  • Online:2018-02-15 Published:2018-11-13

摘要: 针对互联网相似视频内容检测问题,提出了基于短空时变化的鲁棒视频哈希算法。特征提取和特征量化是该算法的两个关键步骤。在特征提取中,与现有基于时空信息融合的特征提取方法相比,该算法的创新性在于充分利用相邻帧之间 局部空域信息的短时变化(简称“短空时变化”)来提取特征。该算法首先构造视频内接球,并以球心为起点对内接球进行划分,获取一系列内接球环,从而捕捉相邻帧的空域信息的短时变化,然后将球环非负矩阵分解系数作为视频内容进行特征表示;在特征量化中,该算法采用改进的曼哈顿量化策略将视频特征映射成二进制的哈希序列,更好地保留了原空间中的近邻关系,提高了量化的准确度。实验结果表明,该算法具有良好的性能。

关键词: 视频哈希,时空信息,非负矩阵分解,相近视频检测,曼哈顿哈希

Abstract: A robust video hashing algorithm based on short-term spatial variations was proposed to detect near-duplicate videos in the Internet.Feature extraction and feature quantization are key steps in this algorithm.In the feature extraction phase,compared to the existing feature extraction methods based on temporal and spatial information fusion,the innovation of the proposed algorithm is to make full use of short-time variations of local spatial information between adjacent frames (referred to “short-term spatial variations”).In the proposed algorithm,inscribed spheres of the video are constructed first,and then a series of spherical tori are obtained by partitioning the inscribed spheres with the center of the sphere as the starting point to capture short-term changes in spatial information between adjacent frames.After that,the decomposition coefficients by non-negative matrix factorization of spherical tori are used as the feature representation of the video.In the feature quantization phase,to map the feature representation into binary hash sequences,the optimized Manhattan hashing strategy is adopted which better reserves the neighborhood structure in the original data space,and thus improves the accuracy of quantization.Experiments were carried out on a video dataset to evaluate the performance of the proposed video hashing method.Experimental results show that the proposed algorithm has good performance.

Key words: Video hashing,Spatio-temporal information,Nonnegative matrix factorization,Near-duplicate video detection,Manhattan hashing

[1] TAN H K,NGO C W,HONG R,et al.Scalable detection of partial near-duplicate videos by visual-temporal consistency[C]∥Proceedings of the 17th ACM International Conference on Multimedia.ACM,2009:145-154.
[2] WU X,HAUPTMANN A G,NGO C W.Practical elimination ofnear-duplicates from web video search[C]∥Proceedings of the 15th ACM International Conference on Multimedia.ACM,2007:218-227.
[3] CHERUBINI M,OLIVEIRA R D,OLIVER N.Understanding near-duplicate videos:a user-centric approach[C]∥Proceedings of the 17th ACM International Conference on Multimedia.ACM,2009:35-44.
[4] HUANG Z,SHEN H T,SHAO J,et al.Practical online near-duplicate subsequence detection for continuous video streams[J].IEEE Transactions on Multimedia,2010,12(5):386-398.
[5] SHEN H T,ZHOU X F,HUANG Z,et al.UQLIPS:a real-time near-duplicate video clip detection system[C]∥Proceedings of the 33rd International Conference on Very Large Data Bases.VLDB Endowment,2007:1374-1377.
[6] WU X,HAUPTMANN A G,NGO C W.Practical elimination of near-duplicates from web video search[C]∥Proceedings of the 15th ACM International Conference on Multimedia.ACM,2007:218-227.
[7] LIU J J,HUANG Z,CAI H Y,et al.Near-duplicate video retrieval[J].ACM Computing Surveys,2013,45(4):1-23.
[8] KONG W H,LI W J,GUO M Y.Manhattan hashing for large-scale image retrieval[C]∥Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval.ACM,2012:45-54.
[9] LEE S,YOO C D.Robust video fingerprinting for content-based video identification[J].IEEE Transactions on Circuits and Systems for Video Technology,2008,18(7):983-988.
[10] ROOVER C D,VLEESCHOUWER C D,LEFèBVRE F,et al.Robust video hashing based on radial projections of key frames[J].IEEE Transactions on Signal Processing,2005,53(10):4020-4037.
[11] LEE S,YOO C D.Video fingerprinting based on centroids ofgradient orientations[C]∥2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.IEEE,2006,2:II.
[12] LEE S,YOO C D,KALKER T.Robust video fingerprintingbased on symmetric pairwise boosting[J].IEEE Transactions on Circuits and Systems for Video Technology,2009,19(9):1379-1388.
[13] CHEN L,STENTIFORD F W M.Video sequence matchingbased on temporal ordinal measurement[J].Pattern Recognition Letters,2008,29(13):1824-1831.
[14] COSKUN B,SANKUR B,MEMON N.Spatio-temporal transform based video hashing[J].IEEE Transactions on Multimedia,2006,8(6):1190-1208.
[15] ESMAEILI M M,WARD R K.Robust video hashing based on temporally informative representative images[C]∥Proceedings of the 32nd International Conference on Coastal Engineering Consumer Electronics (ICCE 2010).Shanghai,China,2010:179-180.
[16] ESMAEILI M M,FATOURECHI M,WARD R K.A robust and fast video copy detection system using content-based fingerprinting[J].IEEE Transactions on Information Forensics and Security,2011,6(1):213-226.
[17] LI M,MONGA V.Robust video hashing via multilinear sub-space projections[J].IEEE Transactions on Image Processing,2012,21(10):4397-4409.
[18] LI M,MONGA V.Twofold video hashing with automatic synchronization[J].IEEE Transactions on Information Forensics and Security,2015,10(8):1727-1738.
[19] SONG J K,YANG Y,HUANG Z,et al.Effective multiple feature hashing for large-scale near-duplicate video retrieval[J].IEEE Transactions on Multimedia,2013,15(8):1997-2008.
[20] LI W J,ZHOU Z H.Learning to hash for big data:current status and future trends[J].Chinese Science Bulletin,2015,60(5/6):485-490.(in Chinese) 李武军,周志华.大数据哈希学习:现状与趋势[J].科学通报,2015,60(5/6):485-490.
[21] NIE X S,CHAI Y E,LIU J,et al.Spherical torus-based video hashing for near-duplicate video detection[J].Science China Information Sciences,2016,59(5):1-3.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 雷丽晖,王静. 可能性测度下的LTL模型检测并行化研究[J]. 计算机科学, 2018, 45(4): 71 -75 .
[2] 孙启,金燕,何琨,徐凌轩. 用于求解混合车辆路径问题的混合进化算法[J]. 计算机科学, 2018, 45(4): 76 -82 .
[3] 张佳男,肖鸣宇. 带权混合支配问题的近似算法研究[J]. 计算机科学, 2018, 45(4): 83 -88 .
[4] 伍建辉,黄中祥,李武,吴健辉,彭鑫,张生. 城市道路建设时序决策的鲁棒优化[J]. 计算机科学, 2018, 45(4): 89 -93 .
[5] 史雯隽,武继刚,罗裕春. 针对移动云计算任务迁移的快速高效调度算法[J]. 计算机科学, 2018, 45(4): 94 -99 .
[6] 周燕萍,业巧林. 基于L1-范数距离的最小二乘对支持向量机[J]. 计算机科学, 2018, 45(4): 100 -105 .
[7] 刘博艺,唐湘滟,程杰仁. 基于多生长时期模板匹配的玉米螟识别方法[J]. 计算机科学, 2018, 45(4): 106 -111 .
[8] 耿海军,施新刚,王之梁,尹霞,尹少平. 基于有向无环图的互联网域内节能路由算法[J]. 计算机科学, 2018, 45(4): 112 -116 .
[9] 崔琼,李建华,王宏,南明莉. 基于节点修复的网络化指挥信息系统弹性分析模型[J]. 计算机科学, 2018, 45(4): 117 -121 .
[10] 王振朝,侯欢欢,连蕊. 抑制CMT中乱序程度的路径优化方案[J]. 计算机科学, 2018, 45(4): 122 -125 .