Distance Metric Learning for Content Identification

• Published in: IEEE Transactions on Information Forensics and Security
• Main Authors: Jang, D, Yoo, CD Yoo, Chang Dong, Kalker, T
• Format: Article in Journal/Newspaper
• Language: English
• Published: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC 2010
• Subjects: DML
• Online Access: http://hdl.handle.net/10203/95203; http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=000284360000029&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=395d0a69a77a4892902e43d8987013d5; https://doi.org/10.1109/TIFS.2010.2064769

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. 전기및전자공학과