Leveraging Computer Vision Application in Visual Arts: A Case Study on the Use of Residual Neural Network to Classify and Analyze Baroque Paintings

TL;DR

This paper explores the use of residual neural networks for classifying and analyzing Baroque paintings, demonstrating their potential in art image retrieval despite domain-specific challenges.

Contribution

It presents a case study applying residual networks to classify a specific Baroque painting and evaluates their effectiveness for art image retrieval tasks.

Findings

Residual networks can extract meaningful features for art classification.

The classifier shows promise in improving image retrieval in online art collections.

Domain-specific challenges still pose hurdles for automated art analysis.

Abstract

With the increasing availability of large digitized fine art collections, automated analysis and classification of paintings is becoming an interesting area of research. However, due to domain specificity, implicit subjectivity, and pervasive nuances that vaguely separate art movements, analyzing art using machine learning techniques poses significant challenges. Residual networks, or variants thereof, are one the most popular tools for image classification tasks, which can extract relevant features for well-defined classes. In this case study, we focus on the classification of a selected painting 'Portrait of the Painter Charles Bruni' by Johann Kupetzky and the analysis of the performance of the proposed classifier. We show that the features extracted during residual network training can be useful for image retrieval within search systems in online art collections.