Forthcoming and Online First Articles

International Journal of Arts and Technology

International Journal of Arts and Technology (IJART)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Arts and Technology (2 papers in press)

Regular Issues

  • Learning of Art Style Using AI and Its Evaluation Based on Psychological Experiments   Order a copy of this article
    by Cong Hung Mai, Ryohei Nakatsu, Naoko Tosa, Takashi Kusumi 
    Abstract: Generative adversarial networks (GANs) are AI technology that can achieve transformation between two image sets. Using GANs, the authors carried out a comparison among several artwork sets with four art styles; Western figurative painting set, Western abstract painting set, Chinese figurative painting set, and abstract image set created by one of the authors. The transformation from a flower photo set to each of these image sets was carried out using GAN, and four image sets, for which their original artworks and art genres were anonymised, were obtained. A psychological experiment was conducted by asking subjects to fill in questionnaires. By analysing the results, the authors found that abstract paintings and figurative paintings are judged to be different and also figurative paintings in the West and East were thought to be similar. These results show that AI can work as an analysis tool to investigate differences among artworks and art genres.
    Keywords: generative adversarial networks; GANs; art genre; art history; style transfer; figurative art; abstract art.
    DOI: 10.1504/IJART.2022.10045168
     
  • Analysis, Attribution, and Authentication of Drawings with Convolutional Neural Networks   Order a copy of this article
    by Steven Frank, Andrea Frank 
    Abstract: We propose an innovative framework for assessing the probability that a subject drawing is the work of a particular artist. While numerous efforts have applied neural networks to classify two-dimensional works of art by style and author, these efforts
    Keywords: convolutional neural network; attribution; authentication; drawings.
    DOI: 10.1504/IJART.2022.10050958