The massive digitization of artworks during the last decades created the need
for categorization, analysis, and management of huge amounts of data related to
abstract concepts, highlighting a challenging problem in the field of computer
science. The rapid progress of artificial intelligence and neural networks has
provided tools and technologies that seem worthy of the challenge. Recognition
of various art features in artworks has gained attention in the deep learning
society. In this paper, we are concerned with the problem of art style
recognition using deep networks. We compare the performance of 8 different deep
architectures (VGG16, VGG19, ResNet50, ResNet152, Inception-V3, DenseNet121,
DenseNet201 and Inception-ResNet-V2), on two different art datasets, including
3 architectures that have never been used on this task before, leading to
state-of-the-art performance. We study the effect of data preprocessing prior
to applying a deep learning model. We introduce a stacking ensemble method
combining the results of first-stage classifiers through a meta-classifier,
with the innovation of a versatile approach based on multiple models that
extract and recognize different characteristics of the input, creating a more
consistent model compared to existing works and achieving state-of-the-art
accuracy on the largest art dataset available (WikiArt - 68,55%). We also
discuss the impact of the data and art styles themselves on the performance of
our models forming a manifold perspective on the problem.

利用深度学习技术，本研究通过 8 种不同的深度网络架构在两个不同的艺术数据集上进行对比实验，并引入一种通过多个模型提取和识别输入的不同特征的可变方法，创造了与现有工作相比更一致的模型，在最大艺术数据集上达到了最新的精确度（WikiArt - 68.55%），同时讨论了数据和艺术风格本身对模型性能的影响。