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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 10
3156-3160

Greedy-asymmetric deep supervised hashing for image retrieval

Zhao Xinxin
Li Yang
Miao Zhuang
Wang Jiabao
Zhang Rui
College of Command & Control Engineering, Army Engineering University of PLA, Nanjing 210007, China
DOI: 10.19734/j.issn.1001-3695.2022.03.0076

Abstract

In recent years, the deep supervised hash retrieval method has been successfully applied to many image retrieval systems. However, the existing methods still have some shortcomings. Firstly, most of the deep hash learning methods used symmetric strategies to train the network, but the training of this strategy was usually time-consuming and difficult to be used in the large-scale hash learning process. Secondly, there was a discrete optimization problem in the hash learning process. Exis-ting methods relaxed this problem and it was difficult to guarantee the optimal solution. In order to solve the above problems, this paper proposed a greedy-asymmetric deep supervised hashing method for image retrieval, which fully combined the advantages of the greedy algorithm and asymmetric strategy to further improve the hash retrieval performance. This article compared 17 state-of-the-art methods on two commonly used datasets. Compared with the state-of-the-art methods, this proposed method increased the mAP in 48 bit setting by 1.3% on CIFAR-10 dataset. And on NUS-WIDE dataset, it increased the mAP in all-bits setting by increased 2.3% on average. The experimental results show that this proposed method can further improve the performance of hash retrieval.

Foundation Support

国家自然科学基金资助项目(61806220)
江苏省自然科学基金资助项目(BK20200581)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.03.0076
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 10
Section: Technology of Graphic & Image
Pages: 3156-3160
Serial Number: 1001-3695(2022)10-044-3156-05

Publish History

[2022-04-27] Accepted Paper
[2022-10-05] Printed Article

Cite This Article

赵昕昕, 李阳, 苗壮, 等. 贪心非对称深度有监督哈希图像检索方法 [J]. 计算机应用研究, 2022, 39 (10): 3156-3160. (Zhao Xinxin, Li Yang, Miao Zhuang, et al. Greedy-asymmetric deep supervised hashing for image retrieval [J]. Application Research of Computers, 2022, 39 (10): 3156-3160. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

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.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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