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| Fabric Color Difference Detection Based on SVM of Multi-dimension Features with Wavelet Kernel |
| Zhiyu Zhou, Rui Xu, Dichong Wu, Yingchun Liu, Zefei Zhu |
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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\%.
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| Cite this article: |
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Zhiyu Zhou,Rui Xu,Dichong Wu, et al. Fabric Color Difference Detection Based on SVM of Multi-dimension Features with Wavelet Kernel[J]. Journal of Fiber Bioengineering and Informatics, 2015, 8(2): 241-248.
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2015, Vol. 8 
