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

Zhu, Yi-hua; Huang, Jiayi; Wu, Tong; Ren, Xueqin

doi:10.1177/1558925021989179

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…Here, g and d were set to 8 and 4, respectively, and the average values of θ (0 deg, 45 deg, 90 deg, and 135 deg) were used to ensure rotation invariance of GLCM features. Four common feature analysis models, namely ASM, 23 contrast, 24 correlation, 25 and entropy, 26 are employed.…”

Section: Sar Image Texture Features and Gray Level Co-occurrence Matrixmentioning

confidence: 99%

Synthetic aperture radar remote sensing scene classification based on fuzzy co-occurrence networks

Fu,

Gong,

Wang

et al. 2024

J. Appl. Rem. Sens.

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The classification of synthetic aperture radar (SAR) image scenes is widely used in military and civilian fields. However, some SAR images pose challenges in feature extraction due to complex scenes and low image resolution, making accurate SAR image classification difficult. To address this, an SAR image scene classification method based on the multi-layer fuzzy convolutional gray level co-occurrence matrix network (ML-FGLCMNet) approach is proposed. The multi-layer convolutional network module in this method can extract low-resolution features from SAR images, and the fuzzy gray-level co-occurrence matrix module combined four sets of secondorder statistics can collect texture features from multiple angles. These features, along with the multi-class support vector machine classifier, are used to achieve SAR image scene classification. The Terra SAR-X and GS-SAR6 datasets were used for experimental analysis and comparison with the full-band channel attention network, multi-feature fusion global-local convolutional network and our method of extracting features with only the multi-layer convolutional network. The experimental results indicate that our method, ML-FGLCMNet, has good accuracy and Kappa index, which can reach more than 90% and 80%, respectively. In the confusion matrix, the classification accuracy of various SAR remote sensing scenes is higher than 0.86. It achieves smaller initial loss, higher training accuracy, and faster convergence compared to the comparative approaches.

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Section: Sar Image Texture Features and Gray Level Co-occurrence Matrixmentioning

confidence: 99%

Synthetic aperture radar remote sensing scene classification based on fuzzy co-occurrence networks

Fu,

Gong,

Wang

et al. 2024

J. Appl. Rem. Sens.

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“…In ref. [9], the cashmere fiber and wool fiber were classified by the textural features obtained from the spatial and frequency domains of the fiber images. In ref.…”

Section: Introductionmentioning

confidence: 99%

Animal Fiber Recognition Based on Feature Fusion of the Maximum Inter-Class Variance

Zhu

Lü

Chen

et al. 2022

AUTEX Research Journal

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Cashmere and wool are common raw materials in the textile industry. The clothes made of cashmere are popular because of the excellent comfort. A system that can quickly and automatically classify the two will improve the efficiency of fiber recognition in the textile industry. We propose a classification method of cashmere and wool fibers based on feature fusion using the maximum inter-class variance. First, the fiber target area is obtained by the preprocessing algorithm. Second, the features of sub-images are extracted through the algorithm of the Discrete Wavelet Transform. It is linearly fused by introducing the weight in the approximate and detailed features. The maximum separability of the feature data can be achieved by the maximum inter-class variance. Finally, different classifiers are used to evaluate the performance of the proposed method. The support vector machine classifier can achieve the highest recognition rate, with an accuracy of 95.20%. The experimental results show that the recognition rate of the fused feature vectors is improved by 6.73% compared to the original feature vectors describing the image. It verifies that the proposed method provides an effective solution for the automatic recognition of cashmere and wool.

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Development of a Computerized Control System Structure and Study of Ultrasonic Wave Propagation in Various Textile Materials

Zdorenko

Zashchepkina

Barylko

et al. 2023

Studies in Systems, Decision and Control

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

Cited by 6 publications

References 15 publications

Synthetic aperture radar remote sensing scene classification based on fuzzy co-occurrence networks

Synthetic aperture radar remote sensing scene classification based on fuzzy co-occurrence networks

Animal Fiber Recognition Based on Feature Fusion of the Maximum Inter-Class Variance

Development of a Computerized Control System Structure and Study of Ultrasonic Wave Propagation in Various Textile Materials

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