Home Journals Articles Updates About
CN
image
收藏切换
Research on Frequency Domain Enhanced Contrastive Learning Method for Art Image Classification
收藏切换
PDF
Qiang ZHANG1, Jiacheng HE2, Lingjun KONG3, *
Radio Communications Technology | 2025, 51(5) : 1046 - 1055
Less
收藏切换
Radio Communications Technology | 2025, 51(5): 1046-1055
• Special Topic:Frontiers in Intelligent Communication, Storage, and Information Processing Technologies •
Research on Frequency Domain Enhanced Contrastive Learning Method for Art Image Classification
Full
Qiang ZHANG1, Jiacheng HE2, Lingjun KONG3, *
Affiliations
  • 1.College of Art, Jinling Institute of Technology, Nanjing 211169, China
  • 2.School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
  • 3.School of Network and Communication Engineering, Jinling Institute of Technology, Nanjing 211169, China
Published: 2025-09-18 doi: 10.3969/j.issn.1003-3114.2025.05.017
Outline
收藏切换

To address the challenges of data scarcity, stylistic diversity, and complex textures in art image classification, a novel self-supervised learning framework is proposed——Frequency-Masked Contrast (F-MaCo). Built upon a dual-branch contrastive learning paradigm, F-MaCo leverages a two-dimensional Discrete Wavelet Transform (DWT) to project images into the frequency domain, enabling dynamic frequency-domain masking augmentation. Additionally, a perceptual loss-driven weighting mechanism is introduced to effectively capture the multi-scale features and rich textures information of art images. Experimental results demonstrate that F-MaCo achieves state-of-the-art performance on four art image datasets—MAMe, Kaokore, Artbench10, and ArtDL—with Top1 accuracies of 73.72%, 77.38%, 58.38%, and 68.31%, respectively, validating its effectiveness and robustness in art image representation learning.

art image classification  /  self-supervised learning  /  wavelet transform  /  contrastive learning  /  frequency domain masking
Qiang ZHANG, Jiacheng HE, Lingjun KONG. Research on Frequency Domain Enhanced Contrastive Learning Method for Art Image Classification[J]. Radio Communications Technology, 2025 , 51 (5) : 1046 -1055 . DOI: 10.3969/j.issn.1003-3114.2025.05.017
Appendix
Less
Year 2025 volume 51 Issue 5
PDF
76
34
Cite this Article
BibTeX
Article Info
doi: 10.3969/j.issn.1003-3114.2025.05.017
  • Receive Date：2025-06-16
  • Online Date：2026-04-17
  • Published：2025-09-18
Article Data
Affiliations
History
  • Received：2025-06-16
Affiliations
    1.College of Art, Jinling Institute of Technology, Nanjing 211169, China
    2.School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
    3.School of Network and Communication Engineering, Jinling Institute of Technology, Nanjing 211169, China
References
Share
https://castjournals.cast.org.cn/joweb/wxdtxjs/EN/10.3969/j.issn.1003-3114.2025.05.017
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House