Fumanal-Idocin, Javier and Andreu-Perez, Javier and Cord贸n, Oscar and Hagras, Hani and Bustince, Humberto (2024) ARTxAI: Explainable Artificial Intelligence Curates Deep Representation Learning for Artistic Images using Fuzzy Techniques. IEEE Transactions on Fuzzy Systems, 32 (4). pp. 1915-1926. DOI https://doi.org/10.1109/tfuzz.2023.3337878 (In Press)
Fumanal-Idocin, Javier and Andreu-Perez, Javier and Cord贸n, Oscar and Hagras, Hani and Bustince, Humberto (2024) ARTxAI: Explainable Artificial Intelligence Curates Deep Representation Learning for Artistic Images using Fuzzy Techniques. IEEE Transactions on Fuzzy Systems, 32 (4). pp. 1915-1926. DOI https://doi.org/10.1109/tfuzz.2023.3337878 (In Press)
Fumanal-Idocin, Javier and Andreu-Perez, Javier and Cord贸n, Oscar and Hagras, Hani and Bustince, Humberto (2024) ARTxAI: Explainable Artificial Intelligence Curates Deep Representation Learning for Artistic Images using Fuzzy Techniques. IEEE Transactions on Fuzzy Systems, 32 (4). pp. 1915-1926. DOI https://doi.org/10.1109/tfuzz.2023.3337878 (In Press)
Abstract
Automatic art analysis employs different image processing techniques to classify and categorize works of art. When working with artistic images, we need to take into account further considerations compared to classical image processing. This is because such artistic paintings change drastically depending on the author, the scene depicted, and their artistic style. This can result in features that perform very well in a given task but do not grasp the whole of the visual and symbolic information contained in a painting. In this paper, we show how the features obtained from different tasks in artistic image classification are suitable to solve other ones of similar nature. We present different methods to improve the generalization capabilities and performance of artistic classification systems. Furthermore, we propose an explainable artificial intelligence method to map known visual traits of an image with the features used by the deep learning model considering fuzzy rules. These rules show the patterns and variables that are relevant to solve each task and how effective is each of the patterns found. Our results show that compared to multi-task learning, our proposed context-aware features can achieve up to 19% more accurate results using the ResNet architecture and 3% when using ConvNext. We also show that some of the features used by these models can be more clearly correlated to visual traits in the original image than others.
Item Type: | Article |
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Uncontrolled Keywords: | Automatic art analysis; Fuzzy rules; Image classification; Fuzzy clustering; Explainable artificial intelligence and Deep learning |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 29 Nov 2023 15:06 |
Last Modified: | 30 Oct 2024 21:47 |
URI: | http://repository.essex.ac.uk/id/eprint/36978 |
Available files
Filename: preprint_ARTxAI.pdf