SNR maximization hashing

Honghai Yu, Pierre Moulin

Research output: Contribution to journalArticlepeer-review


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 languageEnglish (US)
Article number7111289
Pages (from-to)1927-1938
Number of pages12
JournalIEEE Transactions on Information Forensics and Security
Issue number9
StatePublished - Sep 1 2015


  • 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


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