Hashing on nonlinear manifolds

Shen, Fumin, Shen, Chunhua, Shi, Qinfeng, van den Hengel, Anton, Tang, Zhenmin and Shen, Heng Tao (2015) Hashing on nonlinear manifolds. IEEE Transactions on Image Processing, 24 6: 1839-1851. doi:10.1109/TIP.2015.2405340


Author Shen, Fumin
Shen, Chunhua
Shi, Qinfeng
van den Hengel, Anton
Tang, Zhenmin
Shen, Heng Tao
Title Hashing on nonlinear manifolds
Journal name IEEE Transactions on Image Processing   Check publisher's open access policy
ISSN 1057-7149
1941-0042
Publication date 2015-02-24
Year available 2015
Sub-type Article (original research)
DOI 10.1109/TIP.2015.2405340
Open Access Status Not yet assessed
Volume 24
Issue 6
Start page 1839
End page 1851
Total pages 13
Place of publication Piscataway NJ United States
Publisher Institute of Electrical and Electronics Engineers
Language eng
Subject 1712 Software
1704 Computer Graphics and Computer-Aided Design
Abstract Learning-based hashing methods have attracted considerable attention due to their ability to greatly increase the scale at which existing algorithms may operate. Most of these methods are designed to generate binary codes preserving the Euclidean similarity in the original space. Manifold learning techniques, in contrast, are better able to model the intrinsic structure embedded in the original high-dimensional data. The complexities of these models, and the problems with out-of-sample data, have previously rendered them unsuitable for application to large-scale embedding, however. In this paper, how to learn compact binary embeddings on their intrinsic manifolds is considered. In order to address the above-mentioned difficulties, an efficient, inductive solution to the out-of-sample data problem, and a process by which nonparametric manifold learning may be used as the basis of a hashing method are proposed. The proposed approach thus allows the development of a range of new hashing techniques exploiting the flexibility of the wide variety of manifold learning approaches available. It is particularly shown that hashing on the basis of t-distributed stochastic neighbor embedding outperforms state-of-the-art hashing methods on large-scale benchmark data sets, and is very effective for image classification with very short code lengths. It is shown that the proposed framework can be further improved, for example, by minimizing the quantization error with learned orthogonal rotations without much computation overhead. In addition, a supervised inductive manifold hashing framework is developed by incorporating the label information, which is shown to greatly advance the semantic retrieval performance.
Keyword Hashing
Binary code learning
Image retrieval
Manifold learning
Q-Index Code C1
Q-Index Status Confirmed Code
Grant ID 61472063
61473154
FT120100969
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2016 Collection
School of Information Technology and Electrical Engineering Publications
 
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Created: Tue, 10 Mar 2015, 01:01:10 EST by Dr Heng Tao Shen on behalf of School of Information Technol and Elec Engineering