Automatic machine embroidery image color analysis system. Part I: Using Gustafson-Kessel clustering algorithm in embroidery fabric color separation

作者：Kuo Chung Feng Jeffrey^*; Jian Bo Lin; Wu Han Cheng; Peng Kai Ching

来源：Textile Research Journal, 2012, 82(6): 571-583.

DOI：10.1177/0040517511435012

• Gustafson-Kessel
• unsupervised clustering method
• color hybrid median filter
• color bilateral filter
• K-means
• K-medoid
• FCM
• SOM
• cluster validity

This series of studies aims to propose the automatic machine embroidery image color analysis system to solve the problem of lack of manpower for machine embroidery fabric drafting and to shorten drafting time. The studies included three parts: (1) machine embroidery image color separation, (2) search of repeated pattern images, (3) machine embroidery color analysis system integration. %26lt;br%26gt;This study aimed to find the optimal clustering algorithm and cluster validity indices for the automatic color separation process of machine embroidery fabric drafting in order to shorten drafting time. To improve image quality for computer analysis, this study used the color hybrid median filter to filter noise and the color bilateral filter to smoothen fabric and embroidery texture for subsequent color separation. By extracting the color a* component and b* component of the machine embroidery image in CIE L*a*b* color system, this study used the Gustafson-Kessel clustering algorithm for color separation. The Gustafson-Kessel clustering algorithm in machine embroidery image color separation can improve color separation accuracy, and its result is compared with that of the clustering algorithms commonly used in the color separation of color images. This study implemented the chromatography of the color separation results, and used the cluster validity indices to prove that the application of Gustafson-Kessel clustering algorithm in the machine embroidery image color separation system has better results than K-means, K-medoid, fuzzy C-means (FCM), and self-organizing map (SOM) clustering algorithm. The results meet the classifications as expected by human eyes.

• 出版日期2012-4