College of Art and Design, Hunan First Normal University, Changsha 410205, China.
Comput Intell Neurosci. 2022 Jul 5;2022:1478371. doi: 10.1155/2022/1478371. eCollection 2022.
This work aims to improve the feature recognition efficiency of painting images, optimize the style transfer effect of painting images, and save the cost of computer work. First, the theoretical knowledge of painting image recognition and painting style transfer is discussed. Then, lightweight deep learning techniques and their application principles are introduced. Finally, faster convolutional neural network (Faster-CNN) image feature recognition and style transfer models are designed based on a lightweight deep learning model. The model performance is comprehensively evaluated. The research results show that the designed Faster-CNN model has the highest average recognition efficiency of about 28 ms and the lowest of 17.5 ms in terms of feature recognition of painting images. The accuracy of the Faster-CNN model for image feature recognition is about 97% at the highest and 95% at the lowest. Finally, the designed Faster-CNN model can perform style recognition transfer on a variety of painting images. In terms of style recognition transfer efficiency, the highest recognition transfer rate of the designed Faster-CNN model is about 79%, and the lowest is about 77%. This work not only provides an important technical reference for feature recognition and style transfer of painting images but also contributes to the development of lightweight deep learning techniques.
本工作旨在提高绘画图像的特征识别效率，优化绘画图像的风格迁移效果，节省计算机工作成本。首先，讨论了绘画图像识别和绘画风格迁移的理论知识。然后，介绍了轻量级深度学习技术及其应用原理。最后，基于轻量级深度学习模型设计了更快的卷积神经网络（Faster-CNN）图像特征识别和风格迁移模型，并对模型性能进行了综合评估。研究结果表明，所设计的 Faster-CNN 模型在绘画图像的特征识别方面具有最高的平均识别效率，约为 28ms，最低的为 17.5ms。Faster-CNN 模型对图像特征识别的准确性最高约为 97%，最低约为 95%。最后，所设计的 Faster-CNN 模型可以对多种绘画图像进行风格识别迁移。在风格识别迁移效率方面，所设计的 Faster-CNN 模型的最高识别迁移率约为 79%，最低约为 77%。这项工作不仅为绘画图像的特征识别和风格迁移提供了重要的技术参考，也为轻量级深度学习技术的发展做出了贡献。
