Back to Search
Start Over
Regularized Adaboost Learning for Identification of Time-Varying Content.
- Source :
- IEEE Transactions on Information Forensics & Security; Oct2014, Vol. 9 Issue 10, p1606-1616, 11p
- Publication Year :
- 2014
-
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. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 15566013
- Volume :
- 9
- Issue :
- 10
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Information Forensics & Security
- Publication Type :
- Academic Journal
- Accession number :
- 98013505
- Full Text :
- https://doi.org/10.1109/TIFS.2014.2347808