Publication Type
Journal Article
Version
publishedVersion
Publication Date
11-2017
Abstract
Hashing compresses high-dimensional features into compact binary codes. It is one of the promising techniques to support efficient mobile image retrieval, due to its low data transmission cost and fast retrieval response. However, most of existing hashing strategies simply rely on low-level features. Thus, they may generate hashing codes with limited discriminative capability. Moreover, many of them fail to exploit complex and high-order semantic correlations that inherently exist among images. Motivated by these observations, we propose a novel unsupervised hashing scheme, called topic hypergraph hashing (THH), to address the limitations. THH effectively mitigates the semantic shortage of hashing codes by exploiting auxiliary texts around images. In our method, relations between images and semantic topics are first discovered via robust collective non-negative matrix factorization. Afterwards, a unified topic hypergraph, where images and topics are represented with independent vertices and hyperedges, respectively, is constructed to model inherent high-order semantic correlations of images. Finally, hashing codes and functions are learned by simultaneously enforcing semantic consistence and preserving the discovered semantic relations. Experiments on publicly available datasets demonstrate that THH can achieve superior performance compared with several state-of-the-art methods, and it is more suitable for mobile image retrieval.
Keywords
Codes (symbols), topic hypergraph hashing, Factorization, Semantics, Fast retrievals, High dimensional feature, High-order, Hyperedges, Low-level features, Nonnegative matrix factorization, Semantic relations, State-of-the-art methods
Discipline
Computer Sciences | Databases and Information Systems
Research Areas
Information Systems and Management
Publication
IEEE Transactions on Cybernetics
Volume
47
Issue
11
First Page
3941
Last Page
3954
ISSN
2168-2267
Identifier
10.1109/TCYB.2016.2591068
Publisher
IEEE
Citation
ZHU, Lei; SHEN, Jialie; XIE, Liang; and CHENG, Zhiyong.
Unsupervised topic hypergraph hashing for efficient mobile image retrieval. (2017). IEEE Transactions on Cybernetics. 47, (11), 3941-3954.
Available at: https://ink.library.smu.edu.sg/sis_research/3537
Copyright Owner and License
Authors
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/10.1109/TCYB.2016.2591068