Finding Beautiful and Happy Images for Mental Health and Well-being Applications
Ruitao Xie, Connor Qiu, Guoping Qiu

TL;DR
This paper presents a large database of natural images labeled for beauty and happiness, and develops AI models to automatically identify images that can promote mental health and well-being.
Contribution
It introduces the first large-scale database of natural images with beauty and happiness labels and a deep learning model to predict these qualities for mental health applications.
Findings
High correlation between beauty and happiness scores in the database
AI models can accurately predict image beauty and happiness scores
Potential applications for mental health promotion using AI-selected images
Abstract
This paper explores how artificial intelligence (AI) technology can contribute to achieve progress on good health and well-being, one of the United Nations' 17 Sustainable Development Goals. It is estimated that one in ten of the global population lived with a mental disorder. Inspired by studies showing that engaging and viewing beautiful natural images can make people feel happier and less stressful, lead to higher emotional well-being, and can even have therapeutic values, we explore how AI can help to promote mental health by developing automatic algorithms for finding beautiful and happy images. We first construct a large image database consisting of nearly 20K very high resolution colour photographs of natural scenes where each image is labelled with beautifulness and happiness scores by about 10 observers. Statistics of the database shows that there is a good correlation between…
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Taxonomy
TopicsDigital Mental Health Interventions
MethodsHierarchical Average Precision training for Pertinent ImagE Retrieval
