A deep learning approach to clustering visual arts
arXiv:2106.06234 · doi:10.1007/s11263-022-01664-y
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.
Published on Int J Comput Vis (2022)
References in corpus (7)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms
- Detecting People in Artwork with CNNs
- OmniArt: Multi-task Deep Learning for Artistic Data Analysis
- Visual link retrieval and knowledge discovery in painting datasets
- The Cross-Depiction Problem: Computer Vision Algorithms for Recognising Objects in Artwork and in Photographs
- Discovering Visual Patterns in Art Collections with Spatially-consistent Feature Learning