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• W1596827087 abstract "We propose a novel robust hashing algorithm based on signal-to-noise ratio (SNR) maximization to learn compact binary codes, where the SNR metric is used to select a set of projection directions, and one hash bit is extracted from each projection direction. We first motivate this approach under a Gaussian model for the underlying signals, in which case maximizing SNR is equivalent to minimizing the robust hashing error probability. A globally optimal solution can be obtained by solving a generalized eigenvalue problem. We also develop a multibit per projection algorithm to learn longer hash codes when the number of high-SNR projections is limited. The proposed algorithms are tested on both synthetic and real data sets, showing significant performance gains over existing hashing algorithms." @default.
• W1596827087 created "2016-06-24" @default.
• W1596827087 creator A5043000577 @default.
• W1596827087 creator A5062619125 @default.
• W1596827087 date "2015-09-01" @default.
• W1596827087 modified "2023-09-22" @default.
• W1596827087 title "SNR Maximization Hashing" @default.
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• W1596827087 doi "https://doi.org/10.1109/tifs.2015.2436871" @default.
• W1596827087 hasPublicationYear "2015" @default.
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