TY - GEN
T1 - Multi-feature hashing based on SNR maximization
AU - Yu, Honghai
AU - Moulin, Pierre
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/9
Y1 - 2015/12/9
N2 - Hashing algorithms which encode signal content into compact binary codes to preserve similarity, have been extensively studied for applications such as large-scale visual search. However, most existing hashing algorithms work with a single feature type, while combining multiple features is helpful in many vision tasks. In this paper, we propose two multi-feature hashing algorithms based on signal-to-noise ratio (SNR) maximization, where a globally optimal solution is obtained by solving a generalized eigenvalue problem. The first one jointly considers all feature correlations and learns uncorrelated hash functions that maximize SNR, and the second algorithm separately learns hash functions on each individual feature and selects the final hash functions based on the SNR associated with each hash function. The proposed algorithms perform favorably compared to other state-of-the-art multi-feature hashing algorithms on several benchmark datasets.
AB - Hashing algorithms which encode signal content into compact binary codes to preserve similarity, have been extensively studied for applications such as large-scale visual search. However, most existing hashing algorithms work with a single feature type, while combining multiple features is helpful in many vision tasks. In this paper, we propose two multi-feature hashing algorithms based on signal-to-noise ratio (SNR) maximization, where a globally optimal solution is obtained by solving a generalized eigenvalue problem. The first one jointly considers all feature correlations and learns uncorrelated hash functions that maximize SNR, and the second algorithm separately learns hash functions on each individual feature and selects the final hash functions based on the SNR associated with each hash function. The proposed algorithms perform favorably compared to other state-of-the-art multi-feature hashing algorithms on several benchmark datasets.
KW - Hashing
KW - multi-feature
KW - signal-to-noise ratio
UR - http://www.scopus.com/inward/record.url?scp=84956608538&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84956608538&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2015.7351114
DO - 10.1109/ICIP.2015.7351114
M3 - Conference contribution
AN - SCOPUS:84956608538
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 1815
EP - 1819
BT - 2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
PB - IEEE Computer Society
T2 - IEEE International Conference on Image Processing, ICIP 2015
Y2 - 27 September 2015 through 30 September 2015
ER -