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.
Index Terms—Artificial intelligence, art therapy, attention,
deep learning.
The authors are with the Graduate School of Business IT of Kookmin
University, Seoul, Korea (e-mail: {jeung722, yunyi94, lkh5021, kykwahk,
ngkim}@kookmin.ac.kr).
Cite: T. Kim, Y. Yoon, K. Lee, K.-Y. Kwahk, and N. Kim, "Application of Deep Learning in Art Therapy," International Journal of Machine Learning and Computing vol. 11, no. 6, pp. 407-412, 2021.
Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).