Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 7
2221-2228

Deep supervised hashing with performance-aware ranking for multi-label image retrieval

Zhang Zhisheng
Qu Huaijing
Xie Ming
Zhang Hanyuan
School of Information & Electric Engineering, Shandong Jianzhu University, Jinan 250101, China
DOI: 10.19734/j.issn.1001-3695.2023.09.0511

Abstract

Most images in real life have multi-label attributes. For multi-label images, ideally, the retrieved images should be ranked in descending order of similarity to the query image, namely their numbers of labels shared with the query image decrease sequentially. However, most hashing algorithms are mainly designed for the single label image retrieval, and the existing deep supervised hashing algorithms for multi-label image retrieval ignore the ranking performance of hash codes and do not fully utilize the label category information. To solve this problem, this paper proposed a deep supervised hashing with performanceaware ranking method(PRDH), which could effectively perceive and optimize the performance of the model and improve the effect of the multi-label image retrieval. In the hash learning part, this paper designed a ranking optimization loss function to improve the ranking performance of hash codes. At the same time, this paper adopted a spatial partition loss function to divide images with different numbers of shared labels into corresponding Hamming spaces. In order to fully utilize label information, this paper also explicitly proposed using predictive label for Hamming distance calculation in the retrieval stage, and designed a loss function for multi-label classification to achieve supervision and optimization of Hamming distance ranking. A large number of results of the retrieval experiments conducted in three multi-label benchmark datasets show that the evaluation metrics of PRDH outperform the state-of-the-art hashing approaches.

Foundation Support

国家自然科学基金资助项目(62003191)
山东省自然科学基金资助项目(ZR2014FM016)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.09.0511
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 7
Section: Technology of Graphic & Image
Pages: 2221-2228
Serial Number: 1001-3695(2024)07-043-2221-08

Publish History

[2023-12-29] Accepted Paper
[2024-07-05] Printed Article

Cite This Article

张志升, 曲怀敬, 谢明, 等. 具有性能感知排序的深度监督哈希用于多标签图像检索 [J]. 计算机应用研究, 2024, 41 (7): 2221-2228. (Zhang Zhisheng, Qu Huaijing, Xie Ming, et al. Deep supervised hashing with performance-aware ranking for multi-label image retrieval [J]. Application Research of Computers, 2024, 41 (7): 2221-2228. )

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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