本研究的主題為發展一個以感性工學為基礎的布料影像搜尋系統，來輔助服飾設計師在設計布料配色時，能快速有效的搜尋適當的布料影像樣本以及創新配色設計，同樣也提供一般消費者在選擇布料時作為參考。本研究從兩個概念來探討: 一是在感性意象領域中，人們對於布料的感覺相似性如何而來﹖為此我們進行了語意差異法實驗(SD Method)，並以主成分分析(Principle component analys)來建構人們對於布料意象的感性因素空間，藉由階層集群分析與多元尺度法的分析來探討主要影響布料意象的特徵屬性。另一部份則是如何量化適當的布料色彩特徵，配合布料意象特徵，以建立布料圖像特徵空間，讓電腦能與以紀錄辨別。而此以感性工學為基礎的搜尋系統，能經由兩種機能來幫助設計師發展新構想: 藉由調整各種意象語彙的參數來進行高階的感性意象搜尋，以及進行低階的視覺特徵搜尋，包含圖像色彩的改變及相似影像的檢索。此外,藉由類神經網路的學習,系統能根據計算出新樣本的色彩特徵而轉換成新的意象特徵，如此對設計師以及消費者而言，便能以智慧型的搜尋系統來提高影像搜尋的效率與便利。

The objective of this research is to develop an intelligent fabric retrieval system using computer-based Kansei algorithms to assist fashion designers in designing costume textiles, as well as to help consumers find their preferred textile samples quickly and efficiently. There are two major research aspects in this research: The first one addresses the issue of how humans perceive and measure similarity within the domain of the Kansei of images. To understand and describe this mechanism, a subjective experiment is performed. It used the SD method to find humans’ impression of textile, and structured the factor space of pyscological domain through the Principle component analysis. By conferring the result of Hierarchical Cluster Analysis and the Multidimensional scaling (MDS) from the ex-periments, the suitable features of images that influence human Kansei feeling are dis-cussed. The next step is to define the feature space of images according color composi-tion of fabric images. The two feature vectors, Kansei feature vector and an image feature vector, define the feature spaces. The fabric retrieval system, based on human psychologi-cal Kansei and perception, is able to assist designers in creating new ideas through two ways interactively. The first is the high-level Kansei searching algorithm, where predefined impression words are systematically adjusted. The other is the low-level perception query, which includes perception feature modification and image similarity indexing. Moreover, the two ways are correlated with each other through a Neural Network mechanism which is used to correlate the two feature spaces such that the retrieval system can be “intelligent” for the fashion designers and consumers.

1. Huang, Y.R.a.T.S.,“Image Retrieval: Current Techniques, Promising Directions, and Open Issues,”Visual Communication and Image Representation, 1999. 10: p. 39~62.

2. Nagamachi, M.,“Kansei engineering as a powerful consumer-oriented technology for product development,”Applied Ergonomics, 2002. 33(3): p. 289-294.

3. Ishihara, S.I., Keiko,“Nagamachi.etc, An automatic builder for a Kansei Engineer-ing expert system using self-organizing neural networks,”International Journal of Industrial Ergonomics, 1995. 15(1): p. 13-24.

4. Nakada, K., “Kansei engineering research on the design of construction machinery. International Journal of Industrial Ergonomics,”1997. 19(2): p. 129-146.

5. Mojsilovic, A.K.; Jianying Hu; Safranek, R.J.; Ganapathy, S.K., “Matching and Re-trieval Based on the Vocabulary and Grammar of Color Patterns,”Image Processing, IEEE Transactions on, 2000. 9(1): p. 38 -54.

6. Katzo, K.H.a.T., “Query by visual example, content based image retrieval,” Ad-vances in Database Technology, 1992.

7. Niblack, W.,“The QBIC project: Querying images by content using color, texture and shape,”SPIE Storage and Retrieval for Image and Video Data Bases, 1994: p. 172-187.

8. C. Faloutsos, M.F., W. Niblack, D. Petkovic,W. Equitz, and R. Barber, “Efficient and Effective Querying by Image Content,” Technical Report, IBM Research Report, 1993.

9. H. Tamura, S.M., and T. Yamawaki, “Textural features corresponding to visual per-ception. IEEE Trans. Syst., Man, Cybern,”1982. 8: p. 460-473.

10.A. Pentland, R.W.P., and S. Sclaroff, “Photobook: Content-based manipulation of image databases,”Int. J. Comput. Vis, 1996. 18: p. 233-254.

11.Jain, A.G.a.R., “Visual information retrieval,”Commun. ACM, 1997. 40: p. 70~79.

12.Manjunath, W.Y.M.a.B.S., Netra,“A toolbox for navigating large image data-bases,”Proc. IEEE Int. Conf. Image Processing, 1997: p. 568~571.

13.Chang, J.R.S.a.S.,“Visualseek: A fully automated content-based query system,”Proc. ACM Multimedia, 1996: p. 87~98.

14.Dowe, J.,“Content based retrieval in multimedia imaging,”Proc. SPIE. Conf. Storage and Retrieval for Image and Video Databases, 1993.

15.Y. Rui, T.S.H., and S. Mehrotrain,“Content-based image retrieval with relevance feed-back in Mars,”Proc. IEEE Conf. Image Processing, 1997: p. 815~818.

16.Kaori YOSHIDA, T.K.a.T.Y., “Image Retrieval System using Impression Words,” Proc. of 1998 IEEE Int'l Conf. on System Man and Cybernetics, 1998: p. 2780-2784.

17.T. Hayashi, M.H., “Image query by impression words--the IQI system,” IEEE Trans. Consumer Electron, 1998. 44: p. 347~352.

18.Kazuhiro Kuroda, M.H., “An image retrieval system by impression words and spe-cific object names-IRIS, ” Neurocomputing, 2002. 43: p. 259~276.

19.Takagi, H., Noda, T. and Cho, S.-B.,“The psychological space of common media impressions held in a media database retrieval system,”Systems, Man, and Cyber-netics, 1999. IEEE SMC '99 Conference Proceedings, 6: p. 263-268.

20.Fukuda, M.K., M. and Shibata, Y, “Perceptional link method based on dynamic hy-permedia system for design image database system,” System Sciences, Proceedings of the Twenty-Ninth Hawaii International Conference on, 1996. 2: p. 291 -298.

21.M. Mukunoki, M.M., K. Ikeda, “A retrieval method of outdoor scenes using object sketch and an automatic index generation method,”Trans. IEICE J79-D-II (6), 1996: p. 1025~1033.

22.H. Nishiyama, Y.M., “An image retrieval system considering image composition,” Inform. Process. Soc. Japan 37(1), 1996: p. 101-109.

23.Manjunath, W.Y.M.a.B.S., “Texture features and learning similarity,” Proc. IEEE Conf. on ComputerVision and Pattern Recognition, 1996: p. 425~430.

24.Miyahara, M., “Mathematical transform of (r,g,b) color data to munsell (h,s,v) col-ordata,” SPIE Visual Commun.Image Process, 1988. 1001.

25.Xie, X.Y.M., “Hand image segmentation using color and RCE neural networ,” Computational Intelligence in Robotics and AutomationIEEE International Sympo-sium on, 2001. 34: p. 235-250.

26.Huang, J., et al.,“Image indexing using color correlogram,” in Proc.of IEEE Conf. on Computer Vision and Pattern Recognition, 1997.

27.McCamy, C.S., H. Marcus, and J.G. Davidson,“A color-rendition chart,” Journal of Applied Photographic Engineering, 1976. 2(3).

28.http://www.nacs.uci.edu/~wiedeman/cspace/me/inforgb.html.

29.Kung., S.Y., “Digital neural networks,” Englewood Cliffs, N.J: Prentice Hall, 1993.

30.http://www.intl-light.com/handbook/ch01.html.

31.Castleman, K.R.,“Digital Image Processing. Prentice-Hall,Englewood,”1996.

32.D. Androutsos, K.N.P.a.A.N.V.,“A novel vector-based approach to color image re-trieval using a vector angular-based distance measure,” Computer Vision and Image Understanding, 1999. 75: p. 46-58.

33.Hachimura, K.,“Retrieval of paintings using principal color information,”Pattern Recognition, 1996, Proceedings of the 13th International Conference on, 1996. 3: p. 130 -134.

34.Chou, M.-C.S.C.-H.,“A modified version of the K-means algorithm with a distance based on cluster symmetry,”Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2001. 23(6): p. 674 -680.

35.Orille, A.L.S., G. M. A,“Application of neural networks for direct torque control,”Computers & Industrial Engineering, 1999. 37(1-2): p. 391-394.

36.Yun, L.H., Alexander, “Artificial evolution of neural networks and its application to feedback control, ” Artificial Intelligence in Engineering, 1996. 10(2): p. 143-152.

37.Huang, S.-W.H.a.H.C., “A neural network based approach for product form design,” Design Studies, 2002. 23: p. 67~84