Deep Convolutional Embedding for Painting Clustering: Case Study on Picasso’s Artworks

Abstract

Clustering artworks is a very difficult task. Recognizing meaningful patterns in accordance with domain expertise and visual perception, in fact, can be extremely hard. On the other hand, applying traditional clustering and feature reduction techniques to the highly dimensional raw pixel space can be ineffective. To overcome these problems, we propose to use a deep convolutional embedding clustering framework. The model simultaneously optimizes the task of mapping the input pixel data to a latent feature space and the task of finding cluster centroids in this latent space. A quantitative and qualitative preliminary study on a collection of artworks made by Pablo Picasso shows the effectiveness of the model. The proposed method may assist in art-related tasks, in particular visual link retrieval and historical knowledge discovery in painting datasets.