SUN Ziqiang, ZHU Yuesheng, LIU Xiyao. A Novel 3D Video Fingerprinting Algorithm Based on Local Feature Points[J]. Chinese Journal of Electronics, 2018, 27(6): 1192-1199. doi: 10.1049/cje.2018.08.010
Citation: SUN Ziqiang, ZHU Yuesheng, LIU Xiyao. A Novel 3D Video Fingerprinting Algorithm Based on Local Feature Points[J]. Chinese Journal of Electronics, 2018, 27(6): 1192-1199. doi: 10.1049/cje.2018.08.010

A Novel 3D Video Fingerprinting Algorithm Based on Local Feature Points

doi: 10.1049/cje.2018.08.010
Funds:  This work is supported by the Shenzhen Key Lab of Information Theory & Future Network Arch (No.ZDSYS201603311739428) and the Shenzhen Engineering Laboratory of Broadband Wireless Network Security.
More Information
  • Corresponding author: ZHU Yuesheng (corresponding author) received the B.E. degree in radio engineering, M.E. degree in circuits and systems and Ph.D. degree in electronics engineering in 1982, 1989 and 1996, respectively. He is currently working as a professor at the Lab of Communication and Information Security, Shenzhen Graduate School, Peking University. He is a senior member of IEEE, fellow of China Institute of Electronics, and senior member of China Institute of Communications. His interests include digital signal processing, multimedia technology, communication and information security. (Email:zhuys@pkusz.edu.cn)
  • Received Date: 2016-02-24
  • Rev Recd Date: 2016-09-02
  • Publish Date: 2018-11-10
  • Digital rights management of the 3D contents is a crucial open issue in the 3D video industry. A novel robust fingerprinting algorithm is proposed for protecting the copyright of the 3D video. Unlike the existing algorithms extracting visual features separately from the 2D videos and the depth maps, in our algorithm a novel local stereo space is constructed according to the depth information of the pixels around the extracted local feature points in the 2D videos, and the 3D videos are processed in a holistic manner. In the proposed space, the 3D-transform-feature is extracted and aggregated into a feature matrix, and then the compact 3D video fingerprints are obtained from the eigenspace of the matrix. Our comprehensive experiments are conducted on a 3D video database, and the results have demonstrated the robustness and discrimination of the proposed algorithm. Moreover, our fingerprints cost less storage spaces than the existing approaches.
  • loading
  • M.M. Esmaeili, M. Fatourechi and R.K. Ward, “A robust and fast video copy detection system using content-based fingerprinting”, IEEE Transactions on Information Forensics and Security, Vol.6, No.1, pp.213-226, 2011.
    J.T. Robinson, “The KDB-tree: A search structure for large multidimensional dynamic indexes”, Proc. of the 1981 ACM SIGMOD International Conference on Management of Data, Ann Arbor, Michigan, USA, pp.10-18, 1981.
    A. Gionis, P. Indyk and R. Motwani, “Similarity search in high dimensions via hashing”, Very Large Data Bases, Vol.99, No.6, pp.518-529, 1999.
    S. Lian, N. Nikolaidis and H.T. Sencar, “Content-based video copy detection-A survey”, Intelligent Multimedia Analysis for Security Applications, Springer, Berlin, Heidelberg, pp.253-273, 2010.
    R. Mohan, “Video sequence matching”, Proc. of ICASSP, Seattle, WA, USA, pp.3694-3700, 1998.
    A. Hampapur, K. Hyun and R.M. Bolle, “Comparison of sequence matching techniques for video copy detection”, Electronic Imaging, Vol.4676, pp.194-201, 2002.
    J. Sun, J. Wang, J. Zhang, et al., “Video hashing algorithm with weighted matching based on visual saliency”, IEEE Signal Processing Letters, Vol.19, No.6, pp.328-331, 2012.
    X. Liu, J. Sun and J. Liu, “Visual attention based temporally weighting method for video hashing”, IEEE Signal Processing Letters, Vol.20, No.12, pp.1253-1256, 2013.
    S. Lee and C.D. Yoo, “Robust video fingerprinting for contentbased video identification”, IEEE Transactions on Circuits and Systems for Video Technology, Vol.18, No.7, pp.983-988, 2008.
    S. Lee, C.D. Yoo and T. Kalker, “Robust video fingerprinting based on symmetric pairwise boosting”, IEEE Transactions on Circuits and Systems for Video Technology, Vol.19, No.9, pp.1379-1388, 2009.
    Z.Q. Sun, Y.S. Zhu, X.Y. Liu, et al., “A robust video fingerprinting algorithm based on centroid of spatio-temporal gradient orientations”, KSⅡ Transactions on Internet and Information Systems, Vol.7, No.11, pp.2754-2768, 2013.
    J. Mao, G. Xiao, W. Sheng, et al., “A method for video authenticity based on the fingerprint of scene frame”, Neurocomputing, Vol.173, No.3, pp.2022-2032, 2016.
    B. Coskun, B. Sankur and N. Memon, “Spatio-temporal transform based video hashing”, IEEE Transactions on Multimedia, Vol.8, No.6, pp.1190-1208, 2006.
    M.G. Diao, Y.S. Zhu, Z.Q. Sun, et al., “An improved fingerprint algorithm of 3D-DCT for video fingerprinting”, Proc. of the 8th International Symposium on Image and Signal Processing and Analysis, Trieste, Italy, pp.290-295, 2013.
    C. Harris and M. Stephens, “A combined corner and edge detector”, Proc. of 4th Alvey Vision Conference, pp.153-158, 1988.
    A. Joly, O. Buisson and C. Frélicot, “Content-based copy retrieval using distortion-based probabilistic similarity search”, IEEE Trans. on Multimedia, Vol.9, No.2, pp.293-306, 2007.
    J. Law-To, O. Buisson, V. Gouet-Brunet, et al., “Robust voting algorithm based on labels of behavior for video copy detection”, Proc. of ACM Multimedia, New York, USA, pp.835-844, 2006.
    D.G. Lowe, “Distinctive image features from scale-invariant keypoints”, International Journal of Computer Vision, Vol.60, No.2, pp.91-110, 2004.
    Z. Liu, T. Liu, D.C. Gibbon, et al., “Effective and scalable video copy detection”, Proc. of the Multimedia Information Retrieval, New York, NY, USA, pp.119-128, 2010.
    M. Douze, H. Jégou and C. Schmid, “Content-based copy retrieval using distortion-based probabilistic similarity search”, IEEE Trans. on Multimedia, Vol.12, No.4, pp.257-266, 2010.
    H. Jegou, M. Douze and C. Schmid, “Hamming embedding and weak geometric consistency for large scale image search”, Proc. of ECCV, Marseille, France, pp.304-317, 2008.
    H.D. Kim, J.W. Lee, T.W. Oh, et al., “Robust DT-CWT watermarking for DIBR 3D images”, IEEE Transactions on Broadcasting, Vol.58, No.4, pp.533-543, 2012.
    N. Khodabakhshi and M. Hefeeda, “Spider: A system for finding 3D video copies”, ACM TOMM, Vol.9, No.1, pp.7:1-20, 2013.
    S. Mehta and B. Prabhakaran, “3D content fingerprinting”, Proc. of ICIP, Paris, France, pp.4797-4801, 2014.
    Y. Ke and R. Sukthankar, “PCA-SIFT: A more distinctive representation for local image descriptors”, Proc. of CVPR, Washington, DC, USA, pp.506-513, 2004.
    H. Bay, T. Tuytelaars and L. Van Gool, “Surf: Speeded up robust features”, Proc. of ECCV, Graz, Austria, pp.404-417, 2006.
    N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection”, Proc. of CVPR, Washington, DC, USA, pp.886-893, 2005.
    D. Scharstein and R. Szeliski, “A taxonomy and evaluation of dense two-frame stereo correspondence algorithms”, International Journal of Computer Vision, Vol.47, No.1-3, pp.7-42, 2002.
    P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features”, Proc. of CVPR, Kauai, Hawaii, USA, pp.I-I, 2001.
    C.L. Zitnick, S.B. Kang, M. Uyttendaele, et al., “High-quality video view interpolation using a layered representation”, ACM Transactions on Graphics, Vol.23, No.3, pp.600-608, 2004.
    ISO/IEC JTC1/SC29/WG11 MPEG2010 N, 11274:2010, Description of Exploration Experiments in 3D Video Coding.
    R. Rzeszutek, R. Phan and D. Androutsos, “Semi-automatic synthetic depth map generation for video using random walks”, Proc. of International Conference on Multimedia and Expo, Barcelona, Spain, pp.1-6, 2011.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (178) PDF downloads(177) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return