Publication Type
Journal Article
Version
publishedVersion
Publication Date
7-2014
Abstract
Spectral hashing (SpH) is an efficient and simple binary hashing method, which assumes that data are sampled from a multidimensional uniform distribution. However, this assumption is too restrictive in practice. In this paper we propose an improved method, fitted spectral hashing (FSpH), to relax this distribution assumption. Our work is based on the fact that one-dimensional data of any distribution could be mapped to a uniform distribution without changing the local neighbor relations among data items. We have found that this mapping on each PCA direction has certain regular pattern, and could be fitted well by S-curve function (Sigmoid function). With more parameters Fourier function also fits data well. Thus with Sigmoid function and Fourier function, we propose two binary hashing methods: SFSpH and FFSpH. Experiments show that our methods are efficient and outperform state-of-the-art methods.
Keywords
Sigmoid function, Fourier function, Spectral hashing
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Computer Vision and Image Understanding
Volume
124
First Page
3
Last Page
11
ISSN
1077-3142
Identifier
10.1016/j.cviu.2014.01.011
Publisher
Elsevier
Citation
ZHANG, Yong-Dong; WANG, Yu; TANG, Sheng; HOI, Steven C. H.; and LI, Jin-Tao.
FSpH: Fitted spectral hashing for efficient similarity search. (2014). Computer Vision and Image Understanding. 124, 3-11.
Available at: https://ink.library.smu.edu.sg/sis_research/3947
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.cviu.2014.01.011