A Deep Learning Method for Classification of Biophilic Artworks
Purna Kar Jordan J. Bird Yangang Xing Alexander Sumich Andrew Knight Ahmad Lotfi Benedict Carpenter van Barthold
Biophilia is an innate love for living things and nature itself that has been
associated with a positive impact on mental health and well-being. This study
explores the application of deep learning methods for the classification of
Biophilic artwork, in order to learn and explain the different Biophilic
characteristics present in a visual representation of a painting. Using the
concept of Biophilia that postulates the deep connection of human beings with
nature, we use an artificially intelligent algorithm to recognise the different
patterns underlying the Biophilic features in an artwork. Our proposed method
uses a lower-dimensional representation of an image and a decoder model to
extract salient features of the image of each Biophilic trait, such as plants,
water bodies, seasons, animals, etc., based on learnt factors such as shape,
texture, and illumination. The proposed classification model is capable of
extracting Biophilic artwork that not only helps artists, collectors, and
researchers studying to interpret and exploit the effects of mental well-being
on exposure to nature-inspired visual aesthetics but also enables a methodical
exploration of the study of Biophilia and Biophilic artwork for aesthetic
preferences. Using the proposed algorithms, we have also created a gallery of
Biophilic collections comprising famous artworks from different European and
American art galleries, which will soon be published on the Vieunite@ online
community.