Regularized Adaboost Learning for Identification of Time-Varying Content

Honghai Yu, Pierre Moulin

Research output: Contribution to journalArticlepeer-review


This paper proposes a regularized Adaboost algorithm to learn and extract binary fingerprints of time-varying content by filtering and quantizing perceptually significant features. The proposed algorithm extends the recent symmetric pairwise boosting (SPB) algorithm by taking feature sequence correlation into account. An information-theoretic analysis of the SPB algorithm is given, showing that each iteration of SPB maximizes a lower bound on the mutual information between matching fingerprint pairs. Based on the analysis, two practical regularizers are proposed to penalize those filters generating highly correlated filter responses. A learning-theoretic analysis of the regularized Adaboost algorithm is given. The proposed algorithm demonstrates significant performance gains over SPB for both audio and video content identification systems.

Original languageEnglish (US)
Article number6877701
Pages (from-to)1606-1616
Number of pages11
JournalIEEE Transactions on Information Forensics and Security
Issue number10
StatePublished - Oct 2014


  • Algorithm design and analysis
  • Boosting
  • Databases
  • Decoding
  • Measurement
  • Mutual information
  • Upper bound

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications


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