Clustering artworks is difficult for several reasons. On the one hand, recognizing meaningful patterns based on domain knowledge and visual perception is extremely hard. On the other hand, applying traditional clustering and feature reduction techniques to the highly dimensional pixel space can be ineffective. To address these issues, in this paper we propose DELIUS: a DEep learning approach to cLustering vIsUal artS. The method uses a pre-trained convolutional network to extract features and then feeds these features into a deep embedded clustering model, where the task of mapping the input data to a latent space is jointly optimized with the task of finding a set of cluster centroids in this latent space. Quantitative and qualitative experimental results show the effectiveness of the proposed method. DELIUS can be useful for several tasks related to art analysis, in particular visual link retrieval and historical knowledge discovery in painting datasets.
翻译:为了解决这些问题,我们在本文件中建议DLIUS: 一种DELIUS: 一种对 CLusering vIsUal arts S. 的DELIUS 学习方法。 这种方法使用预先训练的共变网络来提取特征,然后将这些特征输入到一个深层嵌入的集成模型中,在这个模型中,将输入数据映射到一个潜藏空间的任务与在这一潜藏空间中寻找一组集聚型类固醇的任务联合优化。 定量和定性实验结果显示了拟议方法的有效性。 DLIUS可用于与艺术分析有关的若干任务,特别是在绘图数据集中的视觉链接检索和历史知识发现。