@Article{JFBI-8-241,
author = {Zhiyu Zhou, Rui Xu, Dichong Wu, Yingchun Liu and  Zefei Zhu},
title = {Fabric Color Difference Detection Based on SVM of Multi-dimension Features with Wavelet Kernel},
journal = {Journal of Fiber Bioengineering and Informatics},
year = {2015},
volume = {8},
number = {2},
pages = {241--248},
abstract = {Traditionally dyed fabric color difference detection is based on the image color characteristics in textile
industry. However, relying solely on the single image color features can't effectively identify dyed fabric
color difference with rich texture characteristics. In order to solve this problem, a new efficient color
difference detection method based on multi-dimensional characteristics of Morlet Wavelet Kernel Support
Vector Machine (MWSVM) is proposed in this paper. Firstly the dyed fabric image to be detected is
divided into some appropriate sub-blocks in the LAB color space. The LAB histograms of the image
in those sub-blocks are extracted. In addition, the Local Binary Pattern (LBP) algorithm is applied to
extract the image texture features in those different divided regions. Then the Grey Relational Grade
(GRG) between the sample image and the detected image is calculated. Finally the LAB histograms,
the LBP features and the GRG are used as the input image data for the MWSVM algorithm to detect
color difference of dyed fabrics. The experimental results show that the proposed method can detect
dyed fabric color difference more efficiently and accurately. The classification accuracy rate as high as
87.5%.},
issn = {2617-8699},
doi = {https://doi.org/10.3993/jfbim00108},
url = {http://global-sci.org/intro/article_detail/jfbi/4703.html}
}