面向图像分类的双域特征联合网络

doi:10.16451/j.cnki.issn1003-6059.202504003

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Abstract The performance improvement of image classification network is constrained due to the reliance on spatial domain features and the neglect of the role of frequency domain features. To address these issues, two-domain feature association networks for image classification(TANet) are proposed. First, a frequency domain feature extraction(FDFE) module is designed. The Fast Fourier Transform is employed to effectively capture the frequency domain detail information and global features in the image, reduce key feature loss, enhance the representation ability of image detail information, and improve the image features extraction ability of the network. Then, the frequency domain attention mechanism(FDAM) is proposed. The multi-scale spatial domain features are taken into account and combined with the Fast Fourier Transform to extract the frequency domain information. Through FDAM, the sensitivity to image details is enhanced, and the contribution of key regions is improved. Subsequently, a two-domain feature association mechanism(TFAM) is designed to fuse the frequency domain features with the spatial domain features. On the basis of retaining spatial domain features, the frequency domain features are utilized to supplement the image detail information as well as the global features and thereby enhance the expression ability of the features. Finally, FDAM is embedded into the residual branch to learn the two-domain features of the input data more effectively. Thus, the attention between local and global information is balanced, the availability of key features is enhanced, and the capability of the network in image classification is improved. Experiments on five public datasets show that TANet enhances the image classification performance of the network by incorporating frequency domain features, extracting image detail information and global features, reducing key feature loss, enhancing the perception of important regions, and improving the expression of features.

Key words： Image Classification Frequency Domain Feature Spatial Domain Feature Fast Fourier Transform Attention Mechanism

Received: 22 January 2025

ZTFLH:

TP391

Fund:National Natural Science Foundation of China(No.61172144), Natural Science Foundation of Liaoning Province(No.20170540426), Key Project of Education Department of Liaoning Province(No.LJYL049)

Corresponding Authors: YUAN Heng, Ph.D., associate professor. Her research interests include image processing, pattern recognition and artificial intelligence.

About author:: YU Dongqi, Master student. His research interests include image processing, pattern recognition and artificial intelligence.GAO Yuan, Master student. His research interests include image processing, pattern recognition and artificial intelligence.

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Cite this article:

YUAN Heng,YU Dongqi,GAO Yuan. Two-Domain Feature Association Networks for Image Classification[J]. Pattern Recognition and Artificial Intelligence, 2025, 38(4): 325-340.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202504003 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2025/V38/I4/325

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