2025-09-26
【學術亮點】建立基於機器學習與深度學習的穿搭色彩調和評估與推薦模型
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【學術亮點】建立基於機器學習與深度學習的穿搭色彩調和評估與推薦模型
Intelligent Husbandry: AI Green Energy Management and Circular Use of Sensing Platform【Bachelor Program in Intellectual Creativity Engineering / Hung-Chung Li / Hung-Chung Li】
智慧農牧場：AI綠能管理與循環利用之感測平台【智慧創意學程李宏中助理教授】
上架日期：2025/09/12
Intelligent Husbandry: AI Green Energy Management and Circular Use of Sensing Platform【Bachelor Program in Intellectual Creativity Engineering / Hung-Chung Li / Hung-Chung Li】
智慧農牧場：AI綠能管理與循環利用之感測平台【智慧創意學程李宏中助理教授】
| 論文篇名 | 英文：Establishing colour harmony evaluation and recommendation model for clothing colour matching based on machine learning and deep learning 中文：建立基於機器學習與深度學習的穿搭色彩調和評估與推薦模型 |
| 期刊名稱 | Fashion and Textiles |
| 發表年份, 卷數, 起迄頁數 | 2025, 12, 27 |
| 作者 | Hung-Chung Li(李宏中)* , Liang-Kai Wang, Yu-Kun Chang, Kuei-Yuan Huang |
| DOI | 10.1186/s40691-025-00433-y |
| 中文摘要 | 適當的色彩組合能提升美學設計品質，並帶來舒適且愉悅的視覺體驗。然而，將現有的色彩和諧模型應用於服裝配色時，引發了現有理論是否需要修正的疑問，顯示有必要從當代美學觀點進一步釐清。本研究透過心理物理實驗，探討現代人對於服裝色彩搭配的和諧感知。結果顯示，現代人對色彩和諧的感知與過往理論存在顯著差異。在應用層面上，本研究以統一的時尚資料集建立了兩種色彩和諧模型，用於服裝搭配的評估：其一為基於色彩和諧規則的模型，能夠準確預測所有符合九種色彩和諧理論的搭配；其二為基於觀察者感知的色彩和諧評估模型，透過半監督式學習方法建構，並符合當代美學偏好。研究中所建立的模型包括支援向量機(SVM)與自訂卷積神經網路(CNN)，能以高效能預測輸入影像的色彩和諧感知；而基於生成對抗網路(GAN)的模型則可提供服裝配色建議。本研究所提出的機器學習與深度學習模型可用於美學判斷，生成服裝色彩推薦，並為時尚與服裝產業的服裝配色提供有效的設計建議。 |
| 英文摘要 | Appropriate colour combinations improved aesthetic design quality and provided a comfortable and pleasant visual experience. However, applying current colour harmony models to clothing colour matching raised doubts about whether the existing theory needed to be refined, requiring further clarification with modern aesthetic perspectives. The study conducted a psychophysical experiment to investigate modern people's perceptions of colour harmony in clothing colour combinations. The results indicated that modern perceptions of colour harmony in clothing differed significantly from previous theories. For applications, two colour harmony models were established with unified fashion datasets for evaluating clothes matching based on colour harmony rules and observers' perceptions. The result showed that the rule-based model could accurately predict all items following nine colour harmony theories, and the perception-based colour harmony evaluation models aligned with contemporary aesthetic preferences were developed with a semi-supervised learning approach. The models, including support vector machines and custom convolutional neural networks, could predict colour harmony perception for the input images with high performance, and the model based on the generative adversarial network could provide colour recommendations for colour matching. The machine learning and deep learning model proposed in the study could be used for aesthetic judgment to generate clothing colour recommendations and provide valid design suggestions for clothing colour matching in the fashion and clothing industry. |
| 發表成果與AI計畫研究主題相關性 | 本研究所開發之穿搭色彩調和推薦模式，主要用以生成影像色彩，並確保所生成之色彩能符合既定的調和條件。此模型的核心技術在於運用生成式AI，將灰階影像轉化為具有美感與一致性色彩的影像結果。在智慧農業的應用場景中，該技術同樣展現出廣泛的價值，例如透過生成式AI生成農作物影像，不僅能解決因環境因素造成影像蒐集不易或色彩品質不穩定的問題，更可藉由色彩生成方式提升農作物影像的真實感與穩定性。此技術可進一步應用於農作物影像資料集之增強、不同成熟度或栽培條件下的色彩模擬，並協助建立更具代表性與準確性的農業AI模型，以提升智慧農業在作物監測、病害偵測上的應用成效。 |
