Title: Transforming design elements and enhancing visual communication via machine learning-based graphic design

Authors: Zhenyuan Hao; Xiaoyang Shen

Addresses: Handan Polytechnic College, Handan, China ' Yanshan University, Qinhuangdao, China

Abstract: This paper proposes convolutional efficient transformer-based image feature extraction. First, it combines convolution from different angles to introduce translation invariance, scale invariance, and locality. Then, a lightweight convolution module with depth-wise convolution and atrous convolution is used to change the traditional processing of the input image of the transformer to accelerate the convergence speed and improve the stability. Aiming at the problem that cycle Generative Adversarial Network (CycleGAN) has low texture clarity when processing images, the convolutional efficient transformer-based image feature extraction algorithm is added to the generator of GAN to enhance the effect of CycleGAN in image style transfer. Results demonstrate that the proposed algorithm performs well regarding top-1 accuracy, number of parameters, floating-point operations and convergence rate. It can effectively analyse and classify visual elements, providing valuable insights and aiding in tasks such as image categorisation, style transfer, generative design or automated design recommendation systems.

Keywords: machine learning; graphic design; transformer; CycleGAN; image style transfer.

DOI: 10.1504/IJCAT.2025.149351

International Journal of Computer Applications in Technology, 2025 Vol.76 No.3/4, pp.166 - 175

Received: 15 Apr 2024
Accepted: 14 Aug 2024
Published online: 27 Oct 2025 *

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