@inproceedings{ab98ddc5577748a8a8e6d4e21c90f2f7,
title = "SNR maximization hashing for learning compact binary codes",
abstract = "In this paper, we propose a novel robust hashing algorithm based on signal-to-noise ratio (SNR) maximization to learn binary codes. We first motivate SNR maximization for robust hashing in a statistical model, under which maximizing SNR minimizes the robust hashing error probability. A globally optimal solution can be obtained by solving a generalized eigenvalue problem. The proposed algorithm is tested on both synthetic and real datasets, showing significant performance gain over existing hashing algorithms.",
keywords = "Robust hashing, SNR maximization, content identification, generalized eigenproblem",
author = "Honghai Yu and Pierre Moulin",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 ; Conference date: 19-04-2014 Through 24-04-2014",
year = "2015",
month = aug,
day = "4",
doi = "10.1109/ICASSP.2015.7178259",
language = "English (US)",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1692--1696",
booktitle = "2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings",
address = "United States",
}