Robust video fingerprinting via structural graphical models

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    Abstract

    Applications of video fingerprinting range from traditional video retrieval and authentication to the more recent problem of anti-piracy search brought about by the emergence of video websites such as Youtube. Video fingerprints offer the potential of identifying in a robust and scalable manner - illegal or undesirable uploads of copyrighted video content. The principal challenge in video fingerprinting is to extract reduced dimensionality descriptors that can withstand incidental spatial and temporal distortions to the video while still allowing the discrimination of distinct videos. To address this fundamental problem, we propose to first represent a video as a graphical structure which can encode temporal relationships between video shots that are crucial to uniquely identifying the video. Next, we leverage ideas from graph theory, namely the normalized cuts graph partitioning method to divide the video representation into sub-graphs. Robust dimensionality reduction applied to these sub-graphs yields the final video hash/fingerprint. Experimental results in the form of receiver operating characteristic (ROC) curves on video databases acquired from YouTube reveal that the proposed video fingerprinting can enable a much more favorable robustness vs. discriminability trade-off over state-of-the art algorithms in video hashing.

    Original languageEnglish (US)
    Title of host publication2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings
    Pages249-252
    Number of pages4
    DOIs
    StatePublished - Dec 1 2012
    Event2012 19th IEEE International Conference on Image Processing, ICIP 2012 - Lake Buena Vista, FL, United States
    Duration: Sep 30 2012Oct 3 2012

    Other

    Other2012 19th IEEE International Conference on Image Processing, ICIP 2012
    CountryUnited States
    CityLake Buena Vista, FL
    Period9/30/1210/3/12

    Fingerprint

    Graph theory
    Authentication
    Websites

    All Science Journal Classification (ASJC) codes

    • Computer Networks and Communications
    • Information Systems

    Cite this

    Li, M., & Monga, V. (2012). Robust video fingerprinting via structural graphical models. In 2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings (pp. 249-252). [6466842] https://doi.org/10.1109/ICIP.2012.6466842
    Li, Mu ; Monga, Vishal. / Robust video fingerprinting via structural graphical models. 2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings. 2012. pp. 249-252
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    Li, M & Monga, V 2012, Robust video fingerprinting via structural graphical models. in 2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings., 6466842, pp. 249-252, 2012 19th IEEE International Conference on Image Processing, ICIP 2012, Lake Buena Vista, FL, United States, 9/30/12. https://doi.org/10.1109/ICIP.2012.6466842

    Robust video fingerprinting via structural graphical models. / Li, Mu; Monga, Vishal.

    2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings. 2012. p. 249-252 6466842.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

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    Li M, Monga V. Robust video fingerprinting via structural graphical models. In 2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings. 2012. p. 249-252. 6466842 https://doi.org/10.1109/ICIP.2012.6466842