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

arXiv 2210.15300 ↗First posted 27 Oct 2022cs.MM cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 36 references that are in the map. First posted on arXiv 27 Oct 2022.

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12 of 36 references.

References · 12

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
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  3. 2019A Comprehensive Survey on Transfer Learningcited by 1,160
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  9. 2015Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Featurecited by 182
  10. 2020Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Futurecited by 83
  11. 2015A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extractioncited by 46
  12. 2019Yet Another Accelerated SGD: ResNet-50 Training on ImageNet in 74.7 secondscited by 12

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