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.

翻译：亲生物性是人与生俱来对生命和自然本身的热爱，已被证实对心理健康和幸福感具有积极影响。本研究探索了深度学习方法在亲生物艺术品分类中的应用，旨在学习并解释绘画视觉表征中蕴含的不同亲生物特征。基于亲生物性理论假设人类与自然存在深层联系，我们采用人工智能算法识别艺术品中亲生物特征的不同潜在模式。所提出的方法通过图像的低维表征与解码器模型，基于形状、纹理、光照等学习因子，提取植物、水体、季节、动物等每种亲生物特质的显著特征。该分类模型不仅能帮助艺术家、收藏家和研究人员解读并利用自然灵感视觉美学对心理健康的促进作用，还能为亲生物性及亲生物艺术品的审美偏好研究提供系统性探索路径。利用所提算法，我们已构建包含欧美多家美术馆著名艺术品的亲生物艺术品图库，该图库即将在Vieunite@在线社区发布。