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Clustering artworks is difficult for several reasons. On the one hand,recognizing meaningful patterns based on domain knowledge and visual perceptionis extremely hard. On the other hand, applying traditional clustering andfeature reduction techniques to the highly dimensional pixel space can beineffective. To address these issues, in this paper we propose DELIUS: a DEeplearning approach to cLustering vIsUal artS. The method uses a pre-trainedconvolutional network to extract features and then feeds these features into adeep embedded clustering model, where the task of mapping the input data to alatent space is jointly optimized with the task of finding a set of clustercentroids in this latent space. Quantitative and qualitative experimentalresults show the effectiveness of the proposed method. DELIUS can be useful forseveral tasks related to art analysis, in particular visual link retrieval andhistorical knowledge discovery in painting datasets.

A deep learning approach to clustering visual arts