A deep learning approach to clustering visual arts

TL;DR

This paper introduces DELIUS, a deep learning-based clustering method for visual arts that leverages pre-trained convolutional features and joint optimization to improve clustering accuracy and facilitate art analysis tasks.

Contribution

The paper presents DELIUS, a novel deep embedded clustering approach specifically designed for artworks, combining feature extraction and clustering in a unified framework.

Findings

Effective clustering of artworks demonstrated through experiments

Improved visual link retrieval in art datasets

Facilitates historical knowledge discovery

Abstract

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.