Unsupervised topic hypergraph hashing for efficient mobile image retrieval
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...
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sg-smu-ink.sis_research-45382018-09-13T07:53:06Z Unsupervised topic hypergraph hashing for efficient mobile image retrieval ZHU, Lei SHEN, Jialie XIE, Liang CHENG, Zhiyong 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. 2017-11-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3537 info:doi/10.1109/TCYB.2016.2591068 https://ink.library.smu.edu.sg/context/sis_research/article/4538/viewcontent/UnsupervisedTopicHypergraphHashing_2016.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University 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 Computer Sciences Databases and Information Systems |
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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 Computer Sciences Databases and Information Systems |
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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 Computer Sciences Databases and Information Systems ZHU, Lei SHEN, Jialie XIE, Liang CHENG, Zhiyong Unsupervised topic hypergraph hashing for efficient mobile image retrieval |
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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. |
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ZHU, Lei SHEN, Jialie XIE, Liang CHENG, Zhiyong |
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ZHU, Lei SHEN, Jialie XIE, Liang CHENG, Zhiyong |
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ZHU, Lei |
title |
Unsupervised topic hypergraph hashing for efficient mobile image retrieval |
title_short |
Unsupervised topic hypergraph hashing for efficient mobile image retrieval |
title_full |
Unsupervised topic hypergraph hashing for efficient mobile image retrieval |
title_fullStr |
Unsupervised topic hypergraph hashing for efficient mobile image retrieval |
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Unsupervised topic hypergraph hashing for efficient mobile image retrieval |
title_sort |
unsupervised topic hypergraph hashing for efficient mobile image retrieval |
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Institutional Knowledge at Singapore Management University |
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2017 |
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https://ink.library.smu.edu.sg/sis_research/3537 https://ink.library.smu.edu.sg/context/sis_research/article/4538/viewcontent/UnsupervisedTopicHypergraphHashing_2016.pdf |
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