Liu Yu, Bai Haozhe, Wang Jingchao
School of Arts, Chongqing University, Chongqing, China.
PeerJ Comput Sci. 2024 Oct 18;10:e2409. doi: 10.7717/peerj-cs.2409. eCollection 2024.
Digital image processing is a constantly evolving field encompassing a wide range of techniques and applications. Researchers worldwide are continually developing various algorithms across multiple fields to achieve accurate image classification. Advanced computer vision algorithms are crucial for architectural and artistic analysis. The digitalization of art has significantly enhanced the accessibility and conservation of fine-art paintings, yet the risk of art theft remains a significant challenge. Improving art security necessitates the precise identification of fine-art paintings. Although current recognition systems have shown potential, there is significant scope for enhancing their efficiency. We developed an improved recognition system for categorizing fine-art paintings using convolutional transformers, specified by an attention mechanism to enhance focused learning on the data. As part of the most advanced architectures in the deep learning family, transformers are empowered by a multi-head attention mechanism, thus improving learning efficiency. To assess the performance of our model, we compared it with those developed using four pre-trained networks: ResNet50, VGG16, AlexNet, and ViT. Each pre-trained network was integrated into a corresponding state-of-the-art model as the first processing blocks. These four state-of-the-art models were constructed under the transfer learning strategy, one of the most commonly used approaches in this field. The experimental results showed that our proposed system outperformed the other models. Our study also highlighted the effectiveness of using convolutional transformers for learning image features.
数字图像处理是一个不断发展的领域，涵盖了广泛的技术和应用。世界各地的研究人员不断在多个领域开发各种算法，以实现准确的图像分类。先进的计算机视觉算法对建筑和艺术分析至关重要。艺术的数字化显著提高了美术绘画的可及性和保护水平，但艺术品被盗的风险仍然是一个重大挑战。提高艺术安全性需要精确识别美术绘画。尽管当前的识别系统已显示出潜力，但仍有很大的提升效率的空间。我们开发了一种改进的识别系统，用于使用卷积变换器对美术绘画进行分类，该变换器由注意力机制指定，以增强对数据的聚焦学习。作为深度学习家族中最先进的架构之一，变换器由多头注意力机制赋能，从而提高学习效率。为了评估我们模型的性能，我们将其与使用四个预训练网络（ResNet50、VGG16、AlexNet和ViT）开发的模型进行了比较。每个预训练网络都作为第一个处理块集成到相应的最先进模型中。这四个最先进的模型是在迁移学习策略下构建的，这是该领域最常用的方法之一。实验结果表明，我们提出的系统优于其他模型。我们的研究还强调了使用卷积变换器学习图像特征的有效性。
