對設計及製造業來說，掌握色彩流行趨勢是銷售制勝的關鍵因素之一，然而坊間許多機構除了預測過程不夠透明之外，最重要的是無法讓使用者及時取得未來流行色的趨勢，為提供產業界提前掌握流行色彩趨勢，本研究以JAVA程式語言建構一套自動化色彩預測系統。首先將所蒐集的色彩資料利用一個具自我辨識的FCM (Fuzzy C-Means)聚類法進行分色，以處理大量的色彩樣本資料，使其可在短時間之內迅速獲得分色結果，其次再以最小平方差（Least Mean-Square Error, LMSE）使分色後的結果在不同時間點之色彩群間的相近色可自動排列在一起，同時亦以灰關聯分析來找出最接近色彩組合。接著利用灰預測模型進行預測。在灰預測模型的使用上，本研究採用兩種模式，一為正規的灰色預測模型，另一為具有殘差修正的預測模型。為驗證所提方法論的可行性，本研究以此色彩預測系統進行案例研究，預測樣本為Pantone 2014春季到2015秋季共四筆流行色彩資料， 並以Pantone2016春季的流行色彩與以本研究所提方法產生的2016春季流行色彩之結果進行比較。根據研究初步結果顯示，具殘差修正的灰色預測模型具相當高精準度的色彩預測能力，其預測結果的準確率從83.3%-99.4%。除此之外，該模式在均方誤差的值亦相當低從0.000025到0.0277。另一方面，灰關聯分析模式的預測結果的準確率從2.28%-98.9%，此顯示該模式仍有改進的空間。此外，其MSE值的範圍為0.0001-0.5963。這個結果表示本智慧預測系統可以產出穩定的流行色彩預測結果，且所產生的結果可作為業界在流行色上之決策依據。

For design and manufacturing industries, being able to grasp the fashion trend is an essential factor that leads to winning a sale. However, colour predicting process in many institutions are not visible to the public. In order to provide colour trend to industries in advance, a predicting method is proposed in this study. In the method, firstly, a new Fuzzy C-Means clustering algorithm was used to separate the colour data being collected. In this step, two models of colour arrangement were separately applied in this study. One is the least mean-square error (LMSE) model, which was used to place the similar colour clusters within different time point together. At the same time, the other model of grey relational analysis will be used to find the closest colour combination. Finally, the grey model was adopted for predicting these two models. In order to verify the probability of the system, four data announced by Pantone from spring 2014 to fall, 2015 were taken as the predicted samples and the colour for spring 2016 was predicted to compare with that in Pantone spring, 2016. The initial results show that the system has a high accuracy for predicting colour with the LMSE model. The residual modified model constructed with the colour samples rearranged with LMSE has the best-predicted result that ranged from 83.3%- 99.4%. It indicates that the result obtained with the rearranged samples is higher than that without rearrangement. Besides, the accuracy of the grey predicted results with the residual modification would be more precise than the formal grey prediction model. Moreover, the value of mean squared error is quite low, which was ranged from 0.000025-0.0277. On the other hand, the predicted result with GRA model was ranged from 2.28%-98.9%, indicating that the accuracy of the prediction result with GRA model still needs slight improvement. Besides, it is easy to find that the MSE value was ranged from 0.0001-0.5963. This result means that this system can generate a stable accuracy outcome in fashion colour prediction. Therefore, the current intelligent predicting system satisfies the criteria of capturing colour in trend for enterprises. Moreover, it enables industries to make decisions for selecting the colour trend.

Ali , N.M., Rashid, N.K.A.M.,& Mustafah, Y.M. (2013). Performance comparison between RGB and HSV color segmentations for road signs detection. Applied Mechanics and Materials, 393, 550–555.
Au, K.F., Choi, T.M., & Yu, Y. (2008). Fashion retail forecasting by evolutionary neural networks. International Journal of Production Economics,114(2), 615–630.
Best, J. (2012). Colour Design Theories and Application. UK: Woodhead Publishing.
Bezdek, J. (1981). Pattern Recognition with Fuzzy Objective Function Algorithm. New York: Plenum Press.
Bhat, V.S., & Pujari, J.D. (2013). Face detection system using HSV color model and morphing operations. International Journal of Current Engineering and Technology, 200-204.
Bieniek, A., & Moga, A. (2000). An efficient watershed algorithm based on connected components. Pattern Recognition,33(6), 907-916.
Bora, D.J., Gupta, A.K., & Khan, F.A. (2015). Comparing the performance of L*A*B* and HSV color spaces with respect to color image segmentation. International Journal of Emerging Technology and Advanced Engineering, 5(2), 192–203.
Bora, D.J., & Gupta, A.K. (2014). A New Approach towards Clustering based Color Image Segmentation. International Journal of Computer Applications, 107(12), 23-30.
Bezdek, J. (1981). Pattern Recognition with Fuzzy Objective Function Algorithm. New York: Plenum Press.
Cai, W., Chen, S., & Zhang, D. (2007). Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recognition, 40(3),825–838.
Crilly, N., Moultrie, J., & Clarkson, P.J. (2009). Shaping things: Intended consumer response and the other determinants of product form. Design Studies, 30(3), 224–254.
Chattopadhyay, A., Gorn, G.J., & Darke, P.(2010). Differences and Similarities in Hue Preferences Between Chinese and Caucasians. In A Krishna (Eds.) Sensory marketing: research on the sensuality of products (pp.219-239). New York: Routledge.
Chien, C.L., &Tseng, D.C. (2011). Color image enhancement with exact HSI color model. International Journal of Innovative Computing, Information and Control, 7(12), 6691-6710.
Chitra, S., & Balakrishnan, G. (2012). Comparative Study for Two Color Spaces HSCbCr and YCbCr in Skin Color Detection. Applied Mathematical Sciences, 6(85), 4229-4238.
Choi, T.M., Hui, C.L., Ng, S.F, & Yu, Y. (2012). Color trend forecasting of fashionable products with very few historical data. IEEE Transactions On Systems, Man, and Cybernetics—Part C: Applications And Reviews, 42(6), 1003–1010.
Choi, T.M., Hui, C.L., & Yu, Y. Intelligent Fashion Forecasting Systems: Models and Applications. In: Yu, Y., Ng, S.F., Hui, C.L., Liu, N., & Choi, T.M., editors. Intelligent Fashion Colour Trend Forecasting Schemes: A Comparative Study. New York: Springer; 2013. 147–160.
Choi, T.M., Yu, Y., & Au, K.F. (2011). A hybrid SARIMA wavelet transform method for sales forecasting. Decision Support System. 51, 130–140.
Deng, J.L. (1982). Control Problems of Grey System. System and Control Letters, 1(5), 288-294.
Deng, J.L. (1989). Introduction to grey system theory. The Journal of Grey System, 1, 1–24.
Ding, C., Ren, X.F., & Zha, H. (2001). Spectral min-max Cut for Graph Partitioning and Data Clustering. Proceedings of the IEEE Intlernational Conference on Data Mining, Netherlandsn, 107-114.
Fu, Z., Yang, J., Hu, W., & Tan, T. (2004). Mixture clustering using multidimensional histograms for skin detection. Proceedings of the 17th International Conference on Pattern Recognition, 549–552.
Fung, C.P. (2003). Manufacturing process optimization for wear property of fiber-reinforced polybutylene terephthalate composites with grey relational analysis. WEAR, 254(3-4), 298-306.
Grau, V., Mewes, A.U.J., Alcaniz, M., Kikinis, R., & Warfield, S.K. (2004). Improved watershed transform for medical image segmentation using prior information. IEEE Transactions on Medical Imaging, 23(4), 447-458.
Hardin, C.L. (2000). Red and yellow, green and blue, warm and cool: Explaining color appearance. Journal of Consciousness Studies, 7(8-9),113-122.
Haris, K., Efstratiadis, S.N., Maglaveras, N., & Katsaggelos, A.K. (1998). Hybrid image segmentation using watersheds and fast region merging. IEEE Transactions On Image Processing. 7(12),1684-1698.
Helander, M.G., Khalid, H.M., Lim, T.Y., Peng, H., & Yang, X. (2013). Emotional needs of car buyers and emotional intent of car designers. Theoretical Issues in Ergonomics Science, 14(5):455–474.
Hsiao, S.W., Chiu, F.Y., & Chen, C.S. (2009). Applying aesthetics measurement to product design. International Journal of Industrial Ergonomics, 38 (11-12), 910-920.
Hsiao, S.W., Lee C.H., Chen, R.Q., Yen, C.H. (2017). An intelligent system for fashion colour prediction based on Fuzzy C-Means and grey theory. Color research and application. 42(2),273-285.
Hsiao, S.W., & Tsai, H.C. (2004). Use of gray system theory in product-color planning. Color research and application, 29(3), 222-231.
Iyatomi, H., Celebi, M.E., Schaefer, G., Tanaka, M. (2011). Automated color calibration method for dermoscopy images. Computerized Medical Imaging and Graphics,35(2), 89-98.
Jumb, V., Sohani, M., & Shrivas, A. (2014). Color Image Segmentation Using K-Means Clustering and Otsu’s Adaptive Thresholding. International Journal of Innovative Technology and Exploring Engineering, 3(9), 72-76.
Jose, M.C.G., Miguel, A.V.R., & Juan, A.G.P. (2010). Detecting skin in face recognition systems: A colour spaces study. Digital Signal Processing, 20(3), 806–823.
Kaushik, P., & Sharma, Y. (2012). Comparison Of Different Image Enhancement Techniques Based Upon Psnr & Mse. International Journal of Applied Engineering Research, 7(11) 2010-2014.
Kolmogorov, V., & Zabih, R. (2006). Graph Cut Algorithms for Binocular Stereo with Occlusions. In: Paragios N, Chen Y, Faugeras O. (eds.) Handbook of Mathematical Models in Computer Vision. Springer, N.Y., 423-437.
Koh, L., & Lee, J. (2012). A study of color differences in women’s ready-to-wear collections from world fashion cities: Intensive study of the fall/winter 2010 collections from New York, London, Milan, and Paris. Color Research and Application, 38(6), 463–468.
Krinidis, S., Chatzis, V. (2010). A robust fuzzy local information c-means clustering algorithm. IEEE Transactions on image. 9,1328-1337.
Kung, C.Y., & Wen, K.L. (2007). Applying Grey Relational Analysis and Grey Decision-Making to evaluate the relationship between company attributes and its financial performance—A case study of venture capital enterprises in Taiwan. Decision Support Systems, 43(3), 842–852.
Lei, N., & Moon, S.K. (2015). A decision support system for market-driven product positioning and design. Decision Support Systems, 69, 82–91.
Liz, B., & Gaynor, L.G. (2006). Fast fashioning the supply chain: shaping the research agenda. Journal of Fashion Marketing and Management, 10(3), 259-271.
Luomala, H.T. (2010). Exploring consumers’ product-specific colour meanings. Qualitative Market Research: An International Journal, 13(3), 287-308.
Mehrabian, A., & Russell, J.A.(1974). An Approach to Environmental Psychology. Cambridge, MA: The MIT Press.
Narkhede, P.R., & Gokhale, A.V. (2015). Color image segmentation using edge detection and seeded region growing approach for CIELAB and HSV color spaces. In: 2015 Interntional Conference on Industrial Instrumentation and Control, p 28–30.
Ng, H.P., Ong, S.H., Foong. K.W.C., Goh, P.S., Nowinski, W.L. (2006). Medical image segmentation using k-means clustering and improved watershed algorithm. IEEE Southwest Symposium on Image Analysis and Interpretation, 61-65.
Ou, L.C., Luo, M.R., Woodcock, A., & Wright, A. (2004). A study of colour emotion and colour preference. Part II: Colour emotions for two colour combinations. Color Research and Application, 29(4), 292-298.
Ou, L.C., Luo, M.R., Woodcock, A., & Wright, A. (2004). A study of colour emotion and colour preference. Part III: Colour preference modeling. Color Research and Application, 29(5), 381-389.
Rong, J., & Pan, Y.L. (2012). Accuracy Improvement of Graph-Cut Image Segmentation by using Watershed. Advanced Materials Research, 341:546-549.
Sun, H.Q., Luo, Y.J. (2009). Adaptive watershed segmentation of binary particle image. Journal of Microscopy, 233(2), 326–330.
Schwarz, M.W., Cowan, W.B., Beatty, J.C. (1987). An experimental comparison of RGB, YIQ, LAB, HSV and opponent color models. ACM Transactions on Graphics, 6(2),123–158.
Sokolov, E.N., & Boucsein, W.A. (2000). Psychophysiological model of emotion space. Integrative Physiological and Behavioral Science, 35(2), 81-119.
Solli, M., & Lenz, R. (2011). Color emotions for multi-colored images. Color Research and Application, 36(3), 210-221.
Sun, Z.L., Choi, T.M., Au, K.F., & Yu, Y. (2008). Sales forecasting using extreme learning machine with applications in fashion retailing. Decision Support System, 46, 411–419.
Sural, S., Qian, G., & Pramanik, S. (2002). Segmentation and histogram generation using the Hsv color space for image retrieval. Proceedings of IEEE International Conference on Image Processing, 589-592.
Tsai, H.C.,& Hsiao, S.W., & Hung, F.K. (2006). An image evaluation approach for parameter-based product form and color design. Computer-Aided Design, 38(2), 157-171.
Tzeng, G.H., Tsaur, S.H. (1994). The multiple criteria evaluation of grey relation model. The Journal of Grey System, 6, 87-104.
Vicente, S., Kolmogorov, V., & Rother, C. (2008). Graph cut based image segmentation with connectivity priors. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1-8.
Vincent, L., Soille, P. (1991). Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(6), 583-597.
Wang, X.Y., Zhang, X.J., Yang, H.Y., & Bu, J. (2012). A pixel-based color image segmentation using support vector machine and fuzzy C-means. Neural Networks, 33,148–159.
Xiao, X., Bai, B., Xu, N., & Wu, K. (2014). A daptive striping watershed segmentation method for processing microscopic images of overlapping irregular-shaped and multicentre particles. Journal of Microscopy, 258(1), 6-12.
Yu, Y., Choi, T.M., & Hui, C.L. (2011). An intelligent fast sales forecasting model for fashion products. Expert Systems with Applications, 38(6),7373–7379.
Yu, Y., Hui, C.L., & Choi, T.M. (2012). An empirical study of intelligent expert systems on forecasting of fashion color trend. Expert Systems with Applications, 39(4), 4383–4389.
Zheng, D. (2015). A novel method for fabric color transfer. Color Research and Application, 40(3), 304–310.
Zhang, J.F. (2013). The application of color psychological effect on fashion design. Advanced Materials Research, 796, 474–478.
Zhang, J., Pan, R., Gao, W., & Zhu, D. (2015). Automatic detection of layout of color yarns of yarn-dyed fabric. Part 1: Single-system-melange color fabrics. Color Research and Application, 40(6), 626–636.
Zarit, B.D., Super, J.B., & Quek, F.K.H. (1999). Comparison of five color models in skin pixel classification. Proceedings International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, 58-63.

Pantone Report
Pantone. 2013, September. Pantone Fashion Color Report Spring 2014.Retrieved from http://www.pantone.com/pages/fcr/?season=spring&year=2014&from=topNav
Pantone. 2014, February. Pantone Fashion Color Report Fall 2014. Retrieved from http://www.pantone.com/pages/fcr/?season=fall&year=2014&pid=3
Pantone. 2014, September. Pantone Fashion Color Report Spring 2015.Retrieved from http://www.pantone.com/pages/fcr/?season=spring&year=2015&from=topnav
Pantone. 2015, February. Pantone Fashion Color Report Fall 2015. Retrieved from http://www.pantone.com/pages/fcr/?season=fall&year=2015&from=topnav