Style migration of Chinese painting based on VGG-19 and MMD convolutional neural network models
The convolutional neural network(CNN)has been widely used in image recognition due to its powerful capabilities in extracting image features.However,style transfer techniques have primarily focused on Western oil paintings,lacking extensive applications in the context of traditional Chinese art styles,such as Chinese ink paintings.In this study,we aimed to explore the ef-fect of convolutional neural networks on extracting features from traditional Chinese ink paintings by replacing Western oil paint-ings with Chinese ink paintings as style images and using natural landscape photographs as content images.We conducted experi-ments based on the VGG algorithm model and TensorFlow 2 framework,preprocessing the collected dataset by converting pixel val-ues into data matrices.These matrices were then fed into the VGG-19 shallow model for training.We further minimized the differ-ences in distribution feature maps using the Maximum Mean Discrepancy(MMD)technique to enhance the target effect of the con-volutional layers.This approach yielded satisfactory results and can provide valuable insights for further research on style transfer transformations.
convolution layer neural networkVGG-19MMDstyle transfer algorithm
NETL
NSTL
万方数据
