A deep learning approach to clustering visual arts
This addresses clustering challenges in art analysis for tasks like visual link retrieval and historical knowledge discovery, but it is incremental as it builds on existing deep learning techniques.
The paper tackles the problem of clustering visual artworks by proposing DELIUS, a deep learning method that combines pre-trained convolutional features with deep embedded clustering, showing effectiveness in quantitative and qualitative experiments.
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.