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.
Original language | English (US) |
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Article number | 7111289 |
Pages (from-to) | 1927-1938 |
Number of pages | 12 |
Journal | IEEE Transactions on Information Forensics and Security |
Volume | 10 |
Issue number | 9 |
DOIs | |
State | Published - Sep 1 2015 |
Keywords
- Hashing
- SNR maximization
- content identification
- fingerprinting.
- image retrieval
- multi-bit hashing
- robust hashing
ASJC Scopus subject areas
- Safety, Risk, Reliability and Quality
- Computer Networks and Communications