Deep convolutional embedding for digitized painting clustering
This addresses the problem of art-related tasks like visual link retrieval and historical knowledge discovery for art historians and researchers, but it is incremental as it builds on existing deep clustering methods.
The paper tackles the challenge of clustering digitized paintings by proposing a deep convolutional embedding model that jointly optimizes mapping raw images to a latent space and finding cluster centroids, resulting in outperforming other state-of-the-art deep clustering approaches.
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.