Version 1
: Received: 27 October 2022 / Approved: 28 October 2022 / Online: 28 October 2022 (09:37:03 CEST)
How to cite:
Kvak, D. Leveraging Computer Vision Application in Visual Arts: A Case Study on the Use of Residual Neural Network to Classify and Analyze Baroque Paintings. Preprints2022, 2022100448. https://doi.org/10.20944/preprints202210.0448.v1
Kvak, D. Leveraging Computer Vision Application in Visual Arts: A Case Study on the Use of Residual Neural Network to Classify and Analyze Baroque Paintings. Preprints 2022, 2022100448. https://doi.org/10.20944/preprints202210.0448.v1
Kvak, D. Leveraging Computer Vision Application in Visual Arts: A Case Study on the Use of Residual Neural Network to Classify and Analyze Baroque Paintings. Preprints2022, 2022100448. https://doi.org/10.20944/preprints202210.0448.v1
APA Style
Kvak, D. (2022). Leveraging Computer Vision Application in Visual Arts: A Case Study on the Use of Residual Neural Network to Classify and Analyze Baroque Paintings. Preprints. https://doi.org/10.20944/preprints202210.0448.v1
Chicago/Turabian Style
Kvak, D. 2022 "Leveraging Computer Vision Application in Visual Arts: A Case Study on the Use of Residual Neural Network to Classify and Analyze Baroque Paintings" Preprints. https://doi.org/10.20944/preprints202210.0448.v1
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.
Keywords
computational creativity; deep learning; feature extraction; image analysis; machine perception; painting classification; residual networks; transfer learning
Subject
Arts and Humanities, Art
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
