The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Matlamat sistem terapi lukisan berbantukan komputer dalam kerja ini adalah untuk mengaitkan lukisan yang dibuat oleh pelanggan dengan keadaan mental klien dari segi kuantitatif. Kajian kes dijalankan ke atas data eksperimen yang mengandungi kedua-dua lukisan pastel dan skor keadaan mental yang diperoleh daripada klien yang sama dalam program psikoterapi. Untuk melaksanakan perkaitan sedemikian melalui warna, kami menterjemah lukisan kepada ciri warna dengan mengukur warna wakilnya sebagai kadar warna primer. Kadar warna primer warna ditakrifkan daripada warna primer psikologi dengan cara yang menunjukkan kadar sifat emosi warna primer psikologi yang sepatutnya mempengaruhi warna. Untuk mendapatkan beberapa warna bermaklumat sebagai warna yang mewakili lukisan, kami mentakrifkan dua jenis warna: warna anggaran yang diekstrak melalui pengurangan warna dan warna purata kawasan yang dikira daripada warna anggaran. Kaedah analisis warna untuk mengekstrak warna perwakilan daripada setiap lukisan dalam urutan lukisan di bawah keadaan yang sama dibentangkan. Untuk menganggarkan sejauh mana ciri warna dikaitkan dengan keadaan mental serentak, kami mencadangkan kaedah menggunakan klasifikasi pembelajaran mesin. Cara praktikal untuk membina model klasifikasi melalui latihan dan pengesahan pada set data yang sangat kecil dibentangkan. Ketepatan klasifikasi yang dicapai oleh model dianggap sebagai tahap perkaitan ciri warna dengan skor keadaan mental yang diberikan dalam set data. Eksperimen telah dijalankan ke atas data klinikal yang diberikan. Beberapa jenis ciri warna telah dibandingkan dari segi perkaitan dengan keadaan mental yang sama. Hasilnya, kami mendapati ciri warna yang baik dengan tahap perkaitan tertinggi. Selain itu, kadar warna primer terbukti lebih berkesan dalam mewakili warna dari segi psikologi berbanding komponen RGB. Eksperimen memberikan bukti bahawa warna boleh dikaitkan secara kuantitatif dengan keadaan minda manusia.
Satoshi MAEDA
Toyo University
Tadahiko KIMOTO
Toyo University
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Salinan
Satoshi MAEDA, Tadahiko KIMOTO, "Associating Colors with Mental States for Computer-Aided Drawing Therapy" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 12, pp. 2057-2068, December 2023, doi: 10.1587/transinf.2023EDP7022.
Abstract: The aim of a computer-aided drawing therapy system in this work is to associate drawings which a client makes with the client's mental state in quantitative terms. A case study is conducted on experimental data which contain both pastel drawings and mental state scores obtained from the same client in a psychotherapy program. To perform such association through colors, we translate a drawing to a color feature by measuring its representative colors as primary color rates. A primary color rate of a color is defined from a psychological primary color in a way such that it shows a rate of emotional properties of the psychological primary color which is supposed to affect the color. To obtain several informative colors as representative ones of a drawing, we define two kinds of color: approximate colors extracted by color reduction, and area-averaged colors calculated from the approximate colors. A color analysis method for extracting representative colors from each drawing in a drawing sequence under the same conditions is presented. To estimate how closely a color feature is associated with a concurrent mental state, we propose a method of utilizing machine-learning classification. A practical way of building a classification model through training and validation on a very small dataset is presented. The classification accuracy reached by the model is considered as the degree of association of the color feature with the mental state scores given in the dataset. Experiments were carried out on given clinical data. Several kinds of color feature were compared in terms of the association with the same mental state. As a result, we found out a good color feature with the highest degree of association. Also, primary color rates proved more effective in representing colors in psychological terms than RGB components. The experimentals provide evidence that colors can be associated quantitatively with states of human mind.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDP7022/_p
Salinan
@ARTICLE{e106-d_12_2057,
author={Satoshi MAEDA, Tadahiko KIMOTO, },
journal={IEICE TRANSACTIONS on Information},
title={Associating Colors with Mental States for Computer-Aided Drawing Therapy},
year={2023},
volume={E106-D},
number={12},
pages={2057-2068},
abstract={The aim of a computer-aided drawing therapy system in this work is to associate drawings which a client makes with the client's mental state in quantitative terms. A case study is conducted on experimental data which contain both pastel drawings and mental state scores obtained from the same client in a psychotherapy program. To perform such association through colors, we translate a drawing to a color feature by measuring its representative colors as primary color rates. A primary color rate of a color is defined from a psychological primary color in a way such that it shows a rate of emotional properties of the psychological primary color which is supposed to affect the color. To obtain several informative colors as representative ones of a drawing, we define two kinds of color: approximate colors extracted by color reduction, and area-averaged colors calculated from the approximate colors. A color analysis method for extracting representative colors from each drawing in a drawing sequence under the same conditions is presented. To estimate how closely a color feature is associated with a concurrent mental state, we propose a method of utilizing machine-learning classification. A practical way of building a classification model through training and validation on a very small dataset is presented. The classification accuracy reached by the model is considered as the degree of association of the color feature with the mental state scores given in the dataset. Experiments were carried out on given clinical data. Several kinds of color feature were compared in terms of the association with the same mental state. As a result, we found out a good color feature with the highest degree of association. Also, primary color rates proved more effective in representing colors in psychological terms than RGB components. The experimentals provide evidence that colors can be associated quantitatively with states of human mind.},
keywords={},
doi={10.1587/transinf.2023EDP7022},
ISSN={1745-1361},
month={December},}
Salinan
TY - JOUR
TI - Associating Colors with Mental States for Computer-Aided Drawing Therapy
T2 - IEICE TRANSACTIONS on Information
SP - 2057
EP - 2068
AU - Satoshi MAEDA
AU - Tadahiko KIMOTO
PY - 2023
DO - 10.1587/transinf.2023EDP7022
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2023
AB - The aim of a computer-aided drawing therapy system in this work is to associate drawings which a client makes with the client's mental state in quantitative terms. A case study is conducted on experimental data which contain both pastel drawings and mental state scores obtained from the same client in a psychotherapy program. To perform such association through colors, we translate a drawing to a color feature by measuring its representative colors as primary color rates. A primary color rate of a color is defined from a psychological primary color in a way such that it shows a rate of emotional properties of the psychological primary color which is supposed to affect the color. To obtain several informative colors as representative ones of a drawing, we define two kinds of color: approximate colors extracted by color reduction, and area-averaged colors calculated from the approximate colors. A color analysis method for extracting representative colors from each drawing in a drawing sequence under the same conditions is presented. To estimate how closely a color feature is associated with a concurrent mental state, we propose a method of utilizing machine-learning classification. A practical way of building a classification model through training and validation on a very small dataset is presented. The classification accuracy reached by the model is considered as the degree of association of the color feature with the mental state scores given in the dataset. Experiments were carried out on given clinical data. Several kinds of color feature were compared in terms of the association with the same mental state. As a result, we found out a good color feature with the highest degree of association. Also, primary color rates proved more effective in representing colors in psychological terms than RGB components. The experimentals provide evidence that colors can be associated quantitatively with states of human mind.
ER -