Department of Textile Engineering, Faculty of Engineering, Yazd University, Yazd, Iran
Abstract
Jeans have increasingly become popular among young people worldwide, just the same as in global markets, and therefore their quality needs to be controlled carefully. This paper deals with the classification of the stone-wash jeans by using k-means clustering algorithm. A total of 306 ready jeans were prepared and the stoned zones of their front and back were imaged under light projection. The K-means clustering algorithm was used to extract jean stone-wash designs. Finally clustering schemes were applied by K-means clustering in order to achieve the optimized condition by running the program one hundred times on the front and the back of the trousers. The clustering validity method was based on Davies-Bouldin Index as the best method. Finally, the result of the above method was compared with the optical method. The results showed that k-means clustering method, applied on stone-wash design, was comparable with the unaided eye observation by 60%.
Mazdak, Z., Payvandy, P., & Alamdar Yazdi, A. A. (2013). Extracting and Clustering Stone-Wash Design in Jeans Images Using K-means Algorithm. Journal of Textile Science and Technology, 2(4), 209-215.
MLA
Zeinab Mazdak; Pedram Payvandy; ALi Asghar Alamdar Yazdi. "Extracting and Clustering Stone-Wash Design in Jeans Images Using K-means Algorithm". Journal of Textile Science and Technology, 2, 4, 2013, 209-215.
HARVARD
Mazdak, Z., Payvandy, P., Alamdar Yazdi, A. A. (2013). 'Extracting and Clustering Stone-Wash Design in Jeans Images Using K-means Algorithm', Journal of Textile Science and Technology, 2(4), pp. 209-215.
VANCOUVER
Mazdak, Z., Payvandy, P., Alamdar Yazdi, A. A. Extracting and Clustering Stone-Wash Design in Jeans Images Using K-means Algorithm. Journal of Textile Science and Technology, 2013; 2(4): 209-215.
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