Style-based Clustering of Visual Artworks and the Play of Neural Style-Representations

TL;DR

This paper explores neural feature representations for clustering visual artworks by style, evaluating various approaches to improve art recommendation, search, and understanding of artistic evolution.

Contribution

It introduces a comprehensive framework for style-based clustering of artworks and analyzes the effectiveness of different neural representations and architectures.

Findings

Neural style representations vary in clustering effectiveness

Large language vision models offer promising results

Insights into feature and architecture choices for style clustering

Abstract

Clustering artworks based on style can have many potential real-world applications like art recommendations, style-based search and retrieval, and the study of artistic style evolution of an artist or in an artwork corpus. We introduce and deliberate over the notion of 'Style-based clustering of visual artworks'. We argue that clustering artworks based on style is largely an unaddressed problem. We explore and devise different neural feature representations - from the style-classification, style-transfer to large language vision models - that can be then used for style-based clustering. Our objective is to assess the relative effectiveness of these devised style-based clustering approaches through qualitative and quantitative analysis by applying them to multiple artwork corpora and curated synthetically styled datasets. Besides providing a broad framework for style-based clustering and evaluation, our analysis provides some key novel insights on feature representations, architectures and implications for style-based clustering.