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

Ying Li and Ye Tang ()
Additional contact information
Ying Li: School of Design, Anhui Polytechnic University, Wuhu 241000, China
Ye Tang: Department of Mechanics, Tianjin University, Tianjin 300350, China

Mathematics, 2023, vol. 11, issue 7, 1-17

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.

Keywords: intelligent algorithms; feature graphic; image generation; deep learning networks; smart museum (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/7/1644/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/7/1644/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:7:p:1644-:d:1110337

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().