摘要
在数字化过程中,如何将不同时期不同风格的未署名画作快速分类成为一个难题。针对以上问题,提出一种通过迁移学习的卷积神经网络的识别分类方法。ResNet-50神经网络模型在ImageNet数据集上完成预训练,通过迁移学习的方式将特征参数迁移到WikiArt数据集上处理选取的23位画家的400幅画作(总共9200幅)。通过迁移学习ResNet-50和DenseNet-201两个神经网络模型对比,发现基于ImageNet数据集在ResNet-50的迁移学习神经网络模型具有良好的特征提取能力,在WikiArt数据集上top-1识别率最高达到81.6%。
In the process of digitization,how to quickly classify unsigned paintings with different styles in different periods is a problem.Hence,a recognition classification method based on convolutional neural network with transfer learning was proposed.ImageNet datasets were performed using the ResNet-50 network model on pre-training,and the feature parameters were transferred to the WikiArt datasets by transfer learning to process 400 paintings of 23 artists(total 9200 paintings).Comparing two neural network models of transfer learning ResNet-50 and DenseNet-201,It is found that the transfer leaning neural network model based on ImageNet datasets in ResNet-50 has good feature extraction ability.On Wikiart dataset,the recognition rate of top-1 is 81.6%.
作者
邓旭
赵连军
郇静
DENG Xu;ZHAO Lian-jun;HUAN Jing(School of Computer Science and Technology,Shandong University of Technology,Zibo 255000,China)
出处
《计算机工程与设计》
北大核心
2021年第3期840-845,共6页
Computer Engineering and Design
关键词
名家画作
数字化
卷积神经网络
迁移学习
图像识别
famous artist’s painting
digitization
CNN
transfer learning
image recognition