Identification method of cashmere and wool based on texture features of GLCM and Gabor

Yaolin Zhu,Xueqin Ren,Tong Wu,Jiayi Huang

doi:10.1177/1558925021989179

Yaolin Zhu, Xueqin Ren + Show 2 more

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https://doi.org/10.1177/1558925021989179

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Abstract

The common texture feature extraction method is only in spatial or frequency domain, leading to insufficient texture information and low accuracy. The main aim of this paper is to present a novel texture feature analysis method based on gray level co-occurrence matrix and Gabor wavelet transform to sufficiently extract texture feature of cashmere and wool fibers. Firstly, the gray level co-occurrence matrix is constructed to calculate the four texture feature vectors including of contrast, angular second moment, dissimilarity and energy in spatial domain, and four texture feature vectors, which are contrast, angular second moment, mean and entropy, in frequency domain is obtained through Gabor wavelet transform and Gray-Scale difference statistics method. Then, because the contrast and angle second moment are used as descriptors of fiber image in both spatial and frequency domain, they are fused respectively by introducing a weight to make linear addition, making eight feature values compose a 6-dimensional feature vector. Finally, these feature vectors are fed into the Fisher classifier. The experimental results show that the identification accuracy of the proposed algorithm is improved by 0.682% compared to use 8-dimensional feature vectors describing the sample image. It verifies that the fused method based on texture feature in spatial and frequency domain is an effective approach to identify fibers of cashmere and wool.

Highlights

Because the structure and morphology of cashmere and wool fibers are very similar, it is difficult to distinguish them
The gray level co-occurrence matrix is constructed to calculate the four texture feature vectors including of contrast, angular second moment, dissimilarity and energy in spatial domain, and four texture feature vectors, which are contrast, angular second moment, mean and entropy, in frequency domain is obtained through Gabor wavelet transform and Gray-Scale difference statistics method
We present an identified method of texture feature based on gray level co-occurrence matrix and Gabor wavelet transform

Summary

Introduction

Because the structure and morphology of cashmere and wool fibers are very similar, it is difficult to distinguish them. Cashmere fiber with the characteristics of expensive price and soft material is a kind of rare animal fiber, so that it has become one of the extremely significant raw material in the textile industry. A number of lawbreakers have used wool as expensive cashmere products to obtain high profits. The identified methods have five categories including physical method, chemical method, biological method, image method and deep convolution network method. In the former three ones, there are commonly identifying methods of microscope method,[1] DNA detection method,[2] solution method,[3] etc. The deep convolution network,[4] a time-consuming method, requires massive

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