Classification of yarn interlacement pattern in fabrics using least square support vector machines |
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Authors: | Anindya Ghosh Tarit Guha R B Bhar |
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Institution: | 2. Government College of Engineering and Textile Technology, Berhampore, West Bengal, 742 101, India 1. Department of Instrumentation, Jadavpur University, Kolkata, 700 032, India
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Abstract: | The purpose of this paper is to suggest an effective and reliable tool that can read through fabric images in the quest of deciphering yarn interlacement patterns by means of Least-Square Support Vector Machines (LS-SVM). A LS-SVM based binary pattern recognition system is formulated for identifying two modes of yarn interlacements viz., warp over weft or warp under weft and accuracy of the classifier was assessed by k-fold cross validation techniques. A comparative study establishes that LS-SVM shows better result than the standard SVM while classifying yarn interlacement patterns in fabrics. The proposed method has the potential to classify yarn interlacement patterns with possibility of extending it to designdecoding of diverse fabrics. |
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