Unsupervised Topic Hypergraph Hashing for Efficient Mobile Image Retrieval

Zhu, Lei, Shen, Jialie, Xie, Liang and Cheng, Zhiyong (2017) Unsupervised Topic Hypergraph Hashing for Efficient Mobile Image Retrieval. IEEE Transactions on Cybernetics, 47 (11). pp. 3941-3954. ISSN 2168-2267

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Official URL: http://dx.doi.org/10.1109/TCYB.2016.2591068

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.

Item Type: Article
Uncontrolled Keywords: High-order semantic correlations, mobile image retrieval, topic hypergraph hashing (THH)
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 13 Aug 2018 10:24
Last Modified: 11 Oct 2019 19:45
URI: http://nrl.northumbria.ac.uk/id/eprint/35279

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