Proceedings of the 2024 International Conference on Artificial Intelligence and Communication (ICAIC 2024)

Comparative Analysis of Deep Learning-Based Models in Sketch Recognition

Authors
Fazhan Liu1, Minxiao Lu2, Wei Zhang3, *
1School of English and Software Engineering, Dalian Jiaotong University, Liaoning, 116000, China
2School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100000, China
3School of Artificial Intelligence, Hebei University of Technology, Tianjin, 300401, China
*Corresponding author. Email: 215306@stu.hebut.edu.cn
Corresponding Author
Wei Zhang
Available Online 23 September 2024.
DOI
10.2991/978-94-6463-512-6_34How to use a DOI?
Keywords
Graph Neural Networks; Sketch Recognition; Computer Vision
Abstract

People usually use hand-drawn sketches to draw simple lines to express or record their ideas and intentions and create images or videos by hand-drawing. For some people who are not specialized in the field of art, the sketches of the same object will vary depending on the artistic style and drawing ability. As a result, they are often highly abstract, which makes the automatic recognition of sketches more difficult compared to other fields. Due to its visualization, sketch recognition has become an important hotspot problem, which effectively improves the efficiency and diversity of generation compared with the traditional manual creation method and becomes an important research direction within computer vision and graphics, and plays a crucial role in the fields of design and visual creation. Existing recognition approaches depend on hand-drawn features and depth features are deficient in recognizing their local information. By using a dataset QuickDraw, which consists of 345 categories with 50 million vector drawings released by Google, this work applies Graph Neural Networks (GNN) to improve the performance, improves the recognition accuracy of targeting sketches drawn by different people.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

Volume Title
Proceedings of the 2024 International Conference on Artificial Intelligence and Communication (ICAIC 2024)
Series
Advances in Intelligent Systems Research
Publication Date
23 September 2024
ISBN
978-94-6463-512-6
ISSN
1951-6851
DOI
10.2991/978-94-6463-512-6_34How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

TY  - CONF
AU  - Fazhan Liu
AU  - Minxiao Lu
AU  - Wei Zhang
PY  - 2024
DA  - 2024/09/23
TI  - Comparative Analysis of Deep Learning-Based Models in Sketch Recognition
BT  - Proceedings of the 2024 International Conference on Artificial Intelligence and Communication (ICAIC 2024)
PB  - Atlantis Press
SP  - 310
EP  - 322
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-512-6_34
DO  - 10.2991/978-94-6463-512-6_34
ID  - Liu2024
ER  -