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".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
L'objectif d'un système de thérapie par le dessin assisté par ordinateur dans ce travail est d'associer les dessins réalisés par un client à son état mental en termes quantitatifs. Une étude de cas est menée sur des données expérimentales contenant à la fois des dessins au pastel et des scores d'état mental obtenus auprès du même client dans un programme de psychothérapie. Pour effectuer une telle association via les couleurs, nous traduisons un dessin en caractéristique de couleur en mesurant ses couleurs représentatives en tant que taux de couleurs primaires. Un taux de couleur primaire d'une couleur est défini à partir d'une couleur primaire psychologique de telle manière qu'il montre un taux de propriétés émotionnelles de la couleur primaire psychologique qui est censé affecter la couleur. Pour obtenir plusieurs couleurs informatives représentatives d'un dessin, nous définissons deux types de couleurs : les couleurs approximatives extraites par réduction de couleur et les couleurs moyennes en zone calculées à partir des couleurs approximatives. Une méthode d'analyse des couleurs permettant d'extraire des couleurs représentatives de chaque dessin dans une séquence de dessins dans les mêmes conditions est présentée. Pour estimer dans quelle mesure une caractéristique de couleur est associée à un état mental concurrent, nous proposons une méthode d'utilisation de la classification par apprentissage automatique. Une manière pratique de construire un modèle de classification par la formation et la validation sur un très petit ensemble de données est présentée. La précision de classification atteinte par le modèle est considérée comme le degré d'association de la caractéristique de couleur avec les scores d'état mental donnés dans l'ensemble de données. Des expériences ont été réalisées sur des données cliniques données. Plusieurs types de caractéristiques de couleur ont été comparés en termes d'association avec le même état mental. En conséquence, nous avons découvert une bonne caractéristique de couleur avec le plus haut degré d’association. En outre, les taux de couleurs primaires se sont révélés plus efficaces pour représenter les couleurs en termes psychologiques que les composants RVB. Les expériences prouvent que les couleurs peuvent être associées quantitativement aux états de l’esprit humain.
Satoshi MAEDA
Toyo University
Tadahiko KIMOTO
Toyo University
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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
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@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},}
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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 -