Application of Deep Learning in Art Therapy

Abstract

—Art therapy is a non-verbal psychotherapy that diagnoses and treats human psychology through the medium of arts. It is focusing on the characteristics that human psychology, especially unconsciousness, appears directly through non-verbal forms rather than specific language. It is used in various fields such as psychotherapy and rehabilitation, and is mainly used for psychotherapy of children who have difficulty expressing their feelings in a specific language. Art therapists interpret symbolic meanings shown in the drawings to diagnose the psychological state of the counselee, and record them as text. But, during this process, interpretation and diagnosis may be affected by the therapist’s subjectivity and experience. Therefore, it is necessary to improve the reliability and objectivity of therapy by automating some of process. For this purpose, in this paper, we propose a CNN(Convolutional Neural Network)-based deep learning method for art therapy. Researches that classify images and generate captions using deep learning models have been actively studied in the field of computer vision and natural language processing. Especially, state of the art has been achieved by applying CNN-based image deep learning models and transfer learning using pre-trained model on large amounts of data. In this paper, we present a CNN model that finds symbolic features in drawings that can be used as a clue in the process of art therapy. Specifically, we apply the image captioning and attention techniques of deep learning to identify psychological features in each drawing. After key features in drawings have been identified and summarized through the proposed methodology, a psychotherapist can make consistent and standardized interpretation based on this in more efficient way. We expect that the proposed methodology may contribute to increase of reliability and objectivity of art therapy.