Regularized Adaboost for content identification

Abstract

This paper proposes a regularized Adaboost learning algorithm to extract binary fingerprints 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. Information and learning theoretic analysis is given. Significant performance gains over SPB are demonstrated for both audio and video fingerprinting.