Fabric Color Difference Detection Based on SVM of Multi-dimension Features with Wavelet Kernel (Journal of Fiber Bioengineering and Informatics, 2015)
Traditionally dyed fabric color difference detection is based on the image color characteristics in textile\rindustry. However, relying solely on the single image color features can't effectively identify dyed fabric\rcolor difference with rich texture characteristics. In order to solve this problem, a new efficient color\rdifference detection method based on multi-dimensional characteristics of Morlet Wavelet Kernel Support\rVector Machine (MWSVM) is proposed in this paper. Firstly the dyed fabric image to be detected is\rdivided into some appropriate sub-blocks in the LAB color space. The LAB histograms of the image\rin those sub-blocks are extracted. In addition, the Local Binary Pattern (LBP) algorithm is applied to\rextract the image texture features in those different divided regions. Then the Grey Relational Grade\r(GRG) between the sample image and the detected image is calculated. Finally the LAB histograms,\rthe LBP features and the GRG are used as the input image data for the MWSVM algorithm to detect\rcolor difference of dyed fabrics. The experimental results show that the proposed method can detect\rdyed fabric color difference more efficiently and accurately. The classification accuracy rate as high as\r87.5%.