Abstract
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 language | English (US) |
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Article number | 6877701 |
Pages (from-to) | 1606-1616 |
Number of pages | 11 |
Journal | IEEE Transactions on Information Forensics and Security |
Volume | 9 |
Issue number | 10 |
DOIs | |
State | Published - Oct 2014 |
Keywords
- 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