Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 9
2866-2871

Unsupervised deep hashing based on semantic transfer for Web image retrieval

Xu Sheng
Chen Shengshuang
Xie Liang
School of Science, Wuhan University of Technology, Wuhan 430070, China
DOI: 10.19734/j.issn.1001-3695.2018.02.0185

Abstract

Most existing Web image retrieval approaches only consider visual features. They ignore the valuable semantics involved in the associated texts, and fail to take advantages of text. This paper proposed a new unsupervised visual hashing approach called STDVH. Firstly, it extracted the semantic information of the training text by spectral clustering. Then, it constructed a deep convolutional neural network to transfer the text semantic information into the learning of the image hash code. At last, it trained the image hash codes and hash functions in a unified framework, and completed the effective retrieval of large-scale image data in low-dimensional Hamming space. Experiments on two publicly available image datasets Wiki and MIR Flickr indicate that the proposed approach can achieve superior performance over other state-of-the-art techniques.

Foundation Support

国家自然科学基金青年基金资助项目(61702388)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.02.0185
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 9
Section: Technology of Graphic & Image
Pages: 2866-2871
Serial Number: 1001-3695(2019)09-066-2866-06

Publish History

[2019-09-05] Printed Article

Cite This Article

许胜, 陈盛双, 谢良. 面向Web图像检索的基于语义迁移的无监督深度哈希 [J]. 计算机应用研究, 2019, 36 (9): 2866-2871. (Xu Sheng, Chen Shengshuang, Xie Liang. Unsupervised deep hashing based on semantic transfer for Web image retrieval [J]. Application Research of Computers, 2019, 36 (9): 2866-2871. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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