Technology of Graphic & Image
|
App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 2
601-606,627

Unsupervised distillation hashing image retrieval method based on equivalent constraint clustering

Miao Zhuang
Wang Yapeng
Li Yang
Zhang Rui
Wang Jiabao
College of Command & Control Engineering, Army Engineering University of PLA, Nanjing 210007, China
DOI: 10.19734/j.issn.1001-3695.2022.06.0274

Abstract

In order to reduce the noise ratio of pseudo labels in unsupervised deep hash learning, this paper proposed a novel two-stage unsupervised distillation hashing method based on equivalent constraint clustering(UDH-ECC). The main idea of this method was to utilize a robust clustering algorithm to annotate unlabeled images, which could get better soft pseudo labels for hash learning. To be specific, in the first stage, it selected a pre-trained teacher network to extract deep image features. Then, it clustered the deep image features by the proposed equivalent constraint clustering algorithm, then assigned hard pseudo labels to unlabeled images by the clustering results. Benefiting from the high accuracy of the hard pseudo labels, this method fine-tuned the teacher network to further adjust the unlabeled dataset. In the second stage, it utilized the predictive probability distribution produced from the fine-tuned teacher network as the soft pseudo labels to train the student hashing network. To further reduce the noise in soft pseudo labels, this paper proposed a distillation hashing method to convert noisy labels into clean hash codes. This paper compared the proposed method with other twelve state-of-the-art methods on three public datasets. The proposed method can outperform other state-of-the-art methods by a large margin, which is 12.7% higher than the TBH method on CIFAR-10, 1.0% higher than the DistillHash method on FLICKR25K, and 16.9% higher than the ETE-GAN method on EuroSAT. Comprehensive experimental results show that the proposed method not only has high performance, but also has good adaptability to variety datasets.

Foundation Support

国家自然科学基金资助项目
国家重点研发计划资助项目
江苏省自然科学基金资助项目
中国博士后科学基金资助项目

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.06.0274
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 2
Section: Technology of Graphic & Image
Pages: 601-606,627
Serial Number: 1001-3695(2023)02-048-0601-06

Publish History

[2022-08-10] Accepted Paper
[2023-02-05] Printed Article

Cite This Article

苗壮, 王亚鹏, 李阳, 等. 一种等量约束聚类的无监督蒸馏哈希图像检索方法 [J]. 计算机应用研究, 2023, 40 (2): 601-606,627. (Miao Zhuang, Wang Yapeng, Li Yang, et al. Unsupervised distillation hashing image retrieval method based on equivalent constraint clustering [J]. Application Research of Computers, 2023, 40 (2): 601-606,627. )

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.

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