  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.
