JIANG Yuxin 1CHEN Lingjie 1GUO Chenyang2
作者信息
- 1. College of Arts and Design,Yunnan University,Kunming 650093
- 2. College of Safety and Emergency Management Engineering,Taiyuan University of Technology,Taiyuan 030024
- 折叠
摘要
Abstract
Intelligent,fast,and accurate painting style classification technologies are of great significance for advancing the intelligent development of art education,art appreciation,painting creation,and cultural heritage preservation.Convolutional neural network(CNN),as the mainstream algorithm for painting style classification,suffers from computational limitations that significantly hinder classification performance.In recent years,Swin Transformer,which has demonstrated outstanding performance in computer vision,has shown new potential in painting style clas-sification tasks;however,its performance is highly dependent on hyperparameter settings.Therefore,this study proposes a GOQPSO-Swin Transformer model,in which Swin Transformer is optimized by generalized opposition quantum-behaved particle swarm optimization(GOQPSO).Experimental results show that the proposed method ex-hibits superior parameter optimization capability,automatically discovering and leveraging effective unconventional hyperparameter combinations.During the parameter optimization phase,the model achieved a fitness of 85.63%,and ultimately attained a Top-1 accuracy of 87.81%on the painting style dataset,outperforming comparative mod-els by 1.42%～13.93%across various metrics.These findings demonstrate that compared to mainstream CNN al-gorithms,Swin Transformer has greater advantages in painting style classification tasks.Moreover,they validate that the proposed GOQPSO-Swin Transformer model can fully exploit the potential of Swin Transformer in this do-main,providing a novel intelligent technical pathway for painting style classification.关键词
广义反向量子粒子群算法/Swin Transformer/画作风格分类/智能化/美术Key words
generalized opposition quantum-behaved particle swarm optimization/Swin Transformer/painting style classification/intellectualization/fine art
