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

KVAK, Daniel. Leveraging Computer Vision Application in Visual Arts: A Case Study on the Use of Residual Neural Network to Classify and Analyze Baroque Paintings. Preprints. Basilej, Švýcarsko: MDPI, 2022, 14 s. ISSN 2310-287X. Dostupné z: https://dx.doi.org/10.20944/preprints202210.0448.v1.

Základní údaje

• Originální název: Leveraging Computer Vision Application in Visual Arts: A Case Study on the Use of Residual Neural Network to Classify and Analyze Baroque Paintings
• Název anglicky: Leveraging Computer Vision Application in Visual Arts: A Case Study on the Use of Residual Neural Network to Classify and Analyze Baroque Paintings
• Autoři: KVAK, Daniel.
• Vydání: Preprints, Basilej, Švýcarsko, MDPI, 2022, 2310-287X.

Další údaje

• Typ výsledku: Článek v odborném periodiku (nerecenzovaný)
• Utajení: není předmětem státního či obchodního tajemství
• WWW: URL
• Doi: http://dx.doi.org/10.20944/preprints202210.0448.v1
• Klíčová slova anglicky: computational creativity; deep learning; feature extraction; image analysis; machine perception; painting classification; residual networks; transfer learning
• Příznaky: Mezinárodní význam

Anotace

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.

Anotace anglicky

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.