Artistic Style Recognition: Combining Deep and Shallow Neural Networks for Painting Classification

Saqib Imran, Rizwan Ali Naqvi, Muhammad Sajid, Tauqeer Safdar Malik, Saif Ullah, Syed Atif Moqurrab () and Dong Keon Yon ()
Additional contact information
Saqib Imran: Department of Computer Science, Muhammad Nawaz Sharif University of Agriculture, Multan 66000, Pakistan
Rizwan Ali Naqvi: Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea
Muhammad Sajid: Department of Computer Science, Air University Islamabad, Multan Campus, Multan 60001, Pakistan
Tauqeer Safdar Malik: Department of Information Technology, Bahauddin Zakariya University, Multan 60800, Pakistan
Saif Ullah: Department of Computer Science, Air University Islamabad, Multan Campus, Multan 60001, Pakistan
Syed Atif Moqurrab: School of Computing, Gachon University, Seongnam 13120, Republic of Korea
Dong Keon Yon: Center for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul 02447, Republic of Korea

Mathematics, 2023, vol. 11, issue 22, 1-27

Abstract: This study’s main goal is to create a useful software application for finding and classifying fine art photos in museums and art galleries. There is an increasing need for tools to swiftly analyze and arrange art collections based on their artistic styles as a result of the digitization of art collections. To increase the accuracy of the style categorization, the suggested technique involves two parts. The input image is split into five sub-patches in the first stage. A DCNN that has been particularly trained for this task is then used to classify each patch individually. A decision-making module using a shallow neural network is part of the second phase. Probability vectors acquired from the first-phase classifier are used to train this network. The results from each of the five patches are combined in this phase to deduce the final style classification for the input image. One key advantage of this approach is employing probability vectors rather than images, and the second phase is trained separately from the first. This helps compensate for any potential errors made during the first phase, improving accuracy in the final classification. To evaluate the proposed method, six various already-trained CNN models, namely AlexNet, VGG-16, VGG-19, GoogLeNet, ResNet-50, and InceptionV3, were employed as the first-phase classifiers. The second-phase classifier was implemented as a shallow neural network. By using four representative art datasets, experimental trials were conducted using the Australian Native Art dataset, the WikiArt dataset, ILSVRC, and Pandora 18k. The findings showed that the recommended strategy greatly surpassed existing methods in terms of style categorization accuracy and precision. Overall, the study assists in creating efficient software systems for analyzing and categorizing fine art images, making them more accessible to the general public through digital platforms. Using pre-trained models, we were able to attain an accuracy of 90.7. Our model performed better with a higher accuracy of 96.5 as a result of fine-tuning and transfer learning.

Keywords: modern art classification; cluster of paintings; deep learning; multi-phase classification; transfer learning; digital humanities (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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