Publication Type

Journal Article

Publication Date

10-2016

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

Data Management and Analytics

Publication

IEEE Transactions on Cybernetics

First Page

1

Last Page

14

ISSN

2168-2267

Identifier

10.1109/TCYB.2016.2591068

Publisher

IEEE

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1109/TCYB.2016.2591068

Share

COinS