  Clustering artworks is difficult for several reasons. On the one hand,
recognizing meaningful patterns in accordance with domain knowledge and visual
perception is extremely difficult. On the other hand, applying traditional
clustering and feature reduction techniques to the highly dimensional pixel
space can be ineffective. To address these issues, we propose to use a deep
convolutional embedding model for digitized painting clustering, in which the
task of mapping the raw input data to an abstract, latent space is jointly
optimized with the task of finding a set of cluster centroids in this latent
feature space. Quantitative and qualitative experimental results show the
effectiveness of the proposed method. The model is also capable of
outperforming other state-of-the-art deep clustering approaches to the same
problem. The proposed method can be useful for several art-related tasks, in
particular visual link retrieval and historical knowledge discovery in painting
datasets.
