Computer Science ›› 2018, Vol. 45 ›› Issue (2): 84-89.doi: 10.11896/j.issn.1002-137X.2018.02.014

Previous Articles     Next Articles

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

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] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[2] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[6] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[7] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[8] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .
[10] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .