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Novel Creation Method of Feature Graphics for Image Generation Based on Deep Learning Algorithms

Author

Listed:
  • Ying Li

    (School of Design, Anhui Polytechnic University, Wuhu 241000, China)

  • Ye Tang

    (Department of Mechanics, Tianjin University, Tianjin 300350, China)

Abstract

In this paper, we propose a novel creation method of feature graphics by deep learning algorithms based on a channel attention module consisting of a separable deep convolutional neural network and an SENet network. The main innovation of this method is that the image feature of sample images is extracted by convolution operation and the key point matrix is obtained by channel weighting calculation to create feature graphics within the channel attention module. The main problem of existing image generation methods is that the complex network training and calculation process affects the accuracy and efficiency of image generation. It greatly reduced the complexity of image generation and improved the efficiency when we trained the image generation network with the feature graphic maps. To verify the superiority of this method, we conducted a comparative experiment with the existing method. Additionally, we explored the influence on the accuracy and efficiency of image generation of the channel number of the weighting matrix based on the test experiment. The experimental results demonstrate that this method highlights the image features of geometric lines, simplifies the complexity of image generation and improves the efficiency. Based on this method, images with more prominent line features are generated from the description text and dynamic graphics are created for the display of the images generated, which can be applied in the construction of smart museums.

Suggested Citation

  • Ying Li & Ye Tang, 2023. "Novel Creation Method of Feature Graphics for Image Generation Based on Deep Learning Algorithms," Mathematics, MDPI, vol. 11(7), pages 1-17, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:7:p:1644-:d:1110337
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    References listed on IDEAS

    as
    1. Lizong Zhang & Haojun Yin & Bei Hui & Sijuan Liu & Wei Zhang, 2022. "Knowledge-Based Scene Graph Generation with Visual Contextual Dependency," Mathematics, MDPI, vol. 10(14), pages 1-20, July.
    2. Mohamed Omri & Sayed Abdel-Khalek & Eied M. Khalil & Jamel Bouslimi & Gyanendra Prasad Joshi, 2022. "Modeling of Hyperparameter Tuned Deep Learning Model for Automated Image Captioning," Mathematics, MDPI, vol. 10(3), pages 1-20, January.
    3. Radu Mărginean & Anca Andreica & Laura Dioşan & Zoltán Bálint, 2020. "Feasibility of Automatic Seed Generation Applied to Cardiac MRI Image Analysis," Mathematics, MDPI, vol. 8(9), pages 1-18, September.
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    Cited by:

    1. Tao Qian & Ying Li & Jun Chen, 2024. "Nonlinear Perception Characteristics Analysis of Ocean White Noise Based on Deep Learning Algorithms," Mathematics, MDPI, vol. 12(18), pages 1-20, September.

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