Journal of Applied Science and Engineering

Published by Tamkang University Press

1.30

Impact Factor

2.10

CiteScore

Jeng-Jong Lin This email address is being protected from spambots. You need JavaScript enabled to view it.1

1Department of Information Management, Vanung University, Chung-Li, Taiwan 320, R.O.C.


 

Received: November 2, 2005
Accepted: September 2, 2006
Publication Date: September 1, 2007

Download Citation: ||https://doi.org/10.6180/jase.2007.10.3.07  


ABSTRACT


This paper evaluates the efficiency and accuracy of a method for recognizing apparel styling that has been classified into different categories by case-based reasoning (CBR). It shows significant promise for improving the effectiveness of complex and unstructured decision making. Four kinds of apparel styling most often to be adopted for styling design were learned by CBR, which is both a paradigm for computer-based problem-solvers and a model of human cognition. In this study, there are two key match factors included in apparel styling, i.e., “Type” and “Color” respectively. Five basic categories, including coat, shirt, vest, skirt, and trousers, were concluded as a “Type” factor. The adopted color tones for the five basic categories mentioned above were concluded as a “color” factor. However, the design of appropriate case retrieval mechanisms is still challenging. This paper presents a genetic algorithm (GA)-based approach to enhance the case-matching process. The results show that apparel styling classification using CBR agrees approximately with initial expectation.


Keywords: Decision Making, Case-based Reasoning, Styling Classification


REFERENCES


  1. [1] Cosgrave, B., “Costume and Fashion: A Complete History., Hamlyn,” London, pp. 214250 (2003).
  2. [2] Callis, C., “Appearance Programs with Female Chroic Psychiatric Hospital Patients: A Comparison of SixWeek and Nine-Week Treatment Interventions,” Journal of Rehabilitation, Vol. 48, pp. 3439 (1982).
  3. [3] James, W., The Principles of Psychology, New York: Holt, Rinehart and Winston, USA (1890).
  4. [4] Smucker, B. V. and Creekmore, A. M., “Adolescent’s Clothing Conformity, Awareness, and Peer Acceptance,” Home Economics Research Journal, Vol. 1, pp. 9297 (1972).
  5. [5] Kochilon, K., Fashion & Color, Han-Dan Publishing Co. Taipei (1990).
  6. [6] Wood, W. H. and Agogino, A. M., “Case-Based Conceptual Design Information Server for Concurrent Engineering,” Computer-Aided Design, Vol. 28, pp. 361 369 (1996).
  7. [7] Bradley, P., “Case-Based Reasoning: Business Applications,” Communication of the ACM, Vol. 37, pp. 4043 (1994).
  8. [8] Carr, H. and Pomeroy, J., Fashion Design & Product Development, Big Apple Tuttle-Mori Agency, Inc. (1999).
  9. [9] Hearn, D. and Baker, M. P., Computer Graphics, Second Edition, Prentice Hall, Inc. NY (1994).
  10. [10] Brieman, L., Friedman, J. H., Olshen, R. A. and Stone, C. J., Classification and Regression Trees, Belmont, CA: Wadsworth International Group (1984).
  11. [11] Gen, M. and Cheng, R., Genetic Algorithms & Engineering Design, John Wiley & Sons, Inc., NY, USA (1997).
  12. [12] Goldberg, D., Genetic Algorithms in Search, Optimization, and Machine Learning, Reading, MA: Addison-Wesley (1989).
  13. [13] Lin, J. J., “A Genetic Algorithm for Searching Weaving Parameters for Woven Fabrics,” Textile Research Journal, Vol. 73, pp. 105112 (2003).
  14. [14] Haralick, R. M., Shanmugam, K. and Dinstein, I., “Texture Features for Image Classification,” IEEE Trans. Sys. Man Cybernet. SMC-3, pp. 610621 (1973).
  15. [15] Sobus, J., Pourdeyhimi, B., Gerde, J. and Ulcay, Y., “Assessing Changes in Texture Periodicity Due to Appearance Loss in Carpets Gray Level Co-occurrence Analysis,” Textile Research Journal, Vol. 61, pp. 557 567 (1991).
  16. [16] Lin, J. J., “Applying a Co-Occurrence Matrix to Automatic Inspection of Weaving Density for Woven Fabrics,” Textile Research Journal, Vol. 72, pp. 486490 (2002).
  17. [17] Don, H. S., Fu, K. S., Liu, C. R. and Lin, W. C., “Metal Surface Inspection Using Image Processing Techniques,” IEEE Trans. Sys. Man Cybernet. SMS-14, pp. 139146 (1984).
  18. [18] Khotanzad, A. and Kashyap, R. L., “Feature Selection for Texture Recognition Based on Image Synthesis,” IEEE Trans. Sys. Man Cybernet. SMC-17, pp. 1087 1095 (1987).