Distance Metric Learning for Content Identification

This paper considers a distance metric learning (DML) algorithm for a fingerprinting system, which identifies a query content by finding the fingerprint in the database (DB) that measures the shortest distance to the query fingerprint. For a given training set consisting of original and distorted fingerprints, a distance metric equivalent to the l(p) norm of the difference of two linearly projected fingerprints is learned by minimizing the false-positive rate (probability of perceptually dissimilar content to be identified as being similar) for a given false-negative rate (probability of perceptually similar content to be identified as being dissimilar). The learned metric can perform better than the often used l(p) distance and improve the robustness against a set of unexpected distortions. In the experiments, the distance metric learned by the proposed algorithm performed better than those metrics learned by well-known DML algorithms for classification.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2010-12
Language
ENG
Keywords

SPECTRAL SUBBAND MOMENTS; CLASSIFICATION; RECOGNITION; RETRIEVAL; SUBSPACE

Citation

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, v.5, no.4, pp.932 - 944

ISSN
1556-6013
DOI
10.1109/TIFS.2010.2064769
URI
http://hdl.handle.net/10203/95203
Appears in Collection
EE-Journal Papers(저널논문)
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