2022 Fiscal Year Final Research Report
Data-driven mutual conversions of semantic colors based on Bayesian inference for color design
| Project/Area Number |
20K19910
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 61060:Kansei informatics-related
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| Research Institution | Prefectural University of Kumamoto |
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Principal Investigator |
Ishibashi Ken 熊本県立大学, 総合管理学部, 准教授 (70749118)
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| Project Period (FY) |
2020-04-01 – 2023-03-31
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| Keywords | 配色 / ベイズ推定 / 言葉 / 支援 / ツール / デザイン |
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| Outline of Final Research Achievements |
This research proposes a mutual conversion method between color schemes and words, focusing on colors and words. The proposed method supports three types of color schemes: 1) automatic generation of words by inputting color schemes (color scheme → word), 2) automatic generation of color schemes by inputting multiple words (word → color scheme), and 3) automatic generation of derived color schemes by multiplying input color schemes and input words (color scheme + word → color scheme). The proposed method collects color scheme data from the Web, generates a probability density function for each word, and then estimates color schemes based on word input and word estimation based on color scheme input. By incorporating these features into color scheme support tools, color scheme names and search tags can be automatically assigned, and color schemes can be generated (searched) using a variety of words.
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| Free Research Field |
感性情報学
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| Academic Significance and Societal Importance of the Research Achievements |
本研究の成果により，配色生成の簡易化だけでなく，分かりやすい検索タグの自動付与による配色検索の効率化，配色のネーミング支援，既存配色への単語印象の追加による派生配色生成などの幅広い応用が期待できる．さらに，配色特徴の指定と複数単語の対応により，配色支援の自由度を拡張する．これにより，配色と言葉を相互利用する際の柔軟性が高まり，デザイン制作におけるポスターやスライド，アニメーション制作での配色設計，マーケティングにおけるカラーブランディング，芸術活動における表現の裾野を広げることによる文化芸術振興の促進など，多方面で研究成果が役立てられる．
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