Classification of Painting Style Based on Image Feature Extraction

The classification of painting style can help viewers find the works they want to appreciate more conveniently, which has a very important role. This paper realized image feature extraction and classification of paintings based on ResNet50. On the basis of ResNet50, squeeze-and-excitation, and convolutional block attention module (CBAM) attention mechanisms were introduced, and different activation functions were selected for improvement. Then, the effect of this method on painting style classification was studied using the Pandora dataset. It was found that ResNet50 obtained the best classification accuracy under a learning rate of 0.0001, a batch size of 32, and 50 iterations. After combining the CBAM attention mechanism, the accuracy rate was 65.64%, which was 6.77% higher than the original ResNet50 and 2.52% higher than ResNet50+SE. Under different activation functions, ResNet50+CBAM (CeLU) had the most excellent performance, with an accuracy rate of 67.13%, and was also superior to the other classification approaches such as Visual Geometry Group (VGG) 16. The findings prove that the proposed approach is applicable to the style classification of painting works and can be applied in practice.