The cognition of audience to artistic style transfer

Yanru Lyu, Chih Long Lin, Po Hsien Lin, Rungtai Lin*

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

12 Scopus citations

Abstract

Artificial Intelligence (AI) is becoming more popular in various fields, including the area of art creation. Advances in AI technology bring new opportunities and challenges in the creation, experience, and appreciation of art. The neural style transfer (NST) realizes the intelligent conversion of any artistic style using neural networks. However, the artistic style is the product of cognition that involving from visual to feel. The purpose of this paper is to study factors affecting audience cognitive difference and preference on artistic style transfer. Those factors are discussed to investigate the application of the AI generator model in art creation. Therefore, based on the artist’s encoding attributes (color, stroke, texture) and the audience’s decoding cognitive levels (technical, semantic, effectiveness), this study proposed a framework to evaluate artistic style transfer in the perspective of cognition. Thirty-one subjects with a background in art, aesthetics, and design were recruited to participate in the experiment. The experimental process consists of four style groups, including Fauvism, Expressionism, Cubism, and Renaissance. According to the finding in this study, participants can still recognize different artistic styles after transferred by neural networks. Besides, the features of texture and stroke are more impact on the perception of fitness than color. The audience may prefer the samples with high cognition in the semantic and effectiveness levels. The above indicates that through AI automated routine work, the cognition of the audience to artistic style still can be kept and transferred.

Original languageEnglish
Article number3290
JournalApplied Sciences (Switzerland)
Volume11
Issue number7
DOIs
StatePublished - 01 04 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • Artificial intelligence
  • Artistic style
  • Cognitive evaluation
  • Neural networks

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