Comparative Analysis of Deep Learning-Based Models in Sketch Recognition

Fazhan Liu; Minxiao Lu; Wei Zhang

doi:10.2991/978-94-6463-512-6_34

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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_34 How 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.

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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_34 How to use a DOI?
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

ris enw bib

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  -

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