ct 22(3): e5

Research Article

AI_deation: A Creative Knowledge Mining Method for Design Exploration

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  • @ARTICLE{10.4108/eetct.v9i3.2685,
        author={George Palamas and Alejandra Mesa Guerra and Liana-Dorina M\`{u}sb\c{c}k},
        title={AI_deation: A Creative Knowledge Mining Method for Design Exploration},
        journal={EAI Endorsed Transactions on Creative Technologies},
        keywords={graphic design, visualization, design exploration, machine learning, gradient-based analysis, design theory, ideation},
  • George Palamas
    Alejandra Mesa Guerra
    Liana-Dorina Møsbæk
    Year: 2022
    AI_deation: A Creative Knowledge Mining Method for Design Exploration
    DOI: 10.4108/eetct.v9i3.2685
George Palamas1,*, Alejandra Mesa Guerra1, Liana-Dorina Møsbæk2
  • 1: Aalborg University Copenhagen, Copenhagen, Denmark
  • 2: Aalborg University Copenhagen
*Contact email: gpa@create.aau.dk


Ideation is a core activity in the design process which begins with a design brief and results in a range of design concepts. However, due to its exploratory nature it is challenging to formalise computationally. Here, we report a creative knowledge mining method that combines design theory with a machine learning approach. This study begins by introducing a graphic design style classification model that acts as a model for the aesthetic evaluation of images. A Grad-CAM technique is used to visualise where our model is looking at in order to detect and interpret visual syntax, such as geometric influences and color gradients, to determine the most influential visual semiotics. Our comparative analysis on two Nordic design referents suggests that our approach can be efficiently used to support and motivate design exploration. Based on these findings, we discuss the prospects of machine vision aided design systems to envisage concepts and possible design paths, but also to support educational objectives.