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2010

Distance Metric Learning for Content Identification

10 years 12 months ago
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 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 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.
Dalwon Jang, Chang Dong Yoo, Ton Kalker
Added 22 May 2011
Updated 22 May 2011
Type Journal
Year 2010
Where TIFS
Authors Dalwon Jang, Chang Dong Yoo, Ton Kalker
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