Robust video fingerprinting via structural graphical models

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

1 Scopus citations


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
Number of pages4
StatePublished - 2012
Event2012 19th IEEE International Conference on Image Processing, ICIP 2012 - Lake Buena Vista, FL, United States
Duration: Sep 30 2012Oct 3 2012

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880


Other2012 19th IEEE International Conference on Image Processing, ICIP 2012
Country/TerritoryUnited States
CityLake Buena Vista, FL

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems


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