摘要
随着艺术数字化的发展,迫切需要准确分析和整理艺术绘画藏品的智能系统,并基于艺术绘画的视觉元素发掘不同艺术属性之间关系。为此,提出基于改进视觉Transformer模型和特征优化算法提高艺术绘画分类的深度学习方法。首先,使用改进双分支视觉Transformer(CrossViT)从艺术绘画图像中提取特征。通过双分支架构提取共享特征,获得多尺度特征表示。设计跨任务融合阶段,使用单独的分支处理特定任务的图元,并通过跨注意力模块交换信息。其后,结合混沌游戏优化(CGO)算法和坚果夹优化器(NO)确定特定最优特征子集。CEC2022基准测试8个函数的算法测试结果验证了所提改进CGO算法的有效性。此外,在SemArt数据集上对艺术绘画进行类型、流派和时期分类任务的实验结果表明,所提方法能够基于不同任务需求准确完成艺术绘画分类,性能优于其他先进方法。
With the advancement of art digitization,there is an urgent need for intelligent systems capable of accurately analyzing and organizing art painting collections and uncovering relationships between different artistic attributes based on visual elements of the paintings.To this end,a deep learning method is proposed for improving art painting classification using an improved Vision Transformer model and feature optimization algorithm.First,features are extracted from art painting images using an improved dual-branch Vision Transformer(CrossViT).Shared features are extracted through a dual-branch architecture,achieving multi-scale feature representations.A cross-task fusion phase is designed,where specific task tokens are processed with separate branches,and information is exchanged through a cross-attention module.Subsequently,a Chaos Game Optimization(CGO)algorithm combined with the Nutcracker Optimizer(NO)is employed to determine an optimal subset of features.The effectiveness of the proposed improved CGO algorithm is vali-dated through algorithmic tests on eight functions from the CEC2022 benchmark.Additionally,experimental results on the SemArt dataset for tasks including type,genre,and period recognition in art painting classification demonstrate that the proposed method accurately accomplishes art painting classification based on various task requirements,outperfor-ming other state-of-the-art methods.
作者
高海燕
丁惠君
GAO Hai-yan;DING Hui-jun(Schoo of Art and Design,Anhui Business And Technology College,Anhui,Hefei 230041;Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging,Shenzhen University,Guangdong Shenzhen 518060)
出处
《贵阳学院学报（自然科学版）》
2024年第3期98-103,共6页
Journal of Guiyang University：Natural Sciences
基金
安徽省级质量工程项目“广告艺术设计专业中国特色学徒制”(项目编号:2022tsxtz005)。
