Identification of cashmere and wool based on LBP and GLCM texture feature selection
There are invalid and redundant features in the texture feature extraction method of cashmere and wool fibers, which leads to the low recognition accuracy. In this paper, a novel texture feature selection method based on local binary pattern, the gray level co-occurrence matrix algorithm and chi-squ...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2023-01-01
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| Series: | Journal of Engineered Fibers and Fabrics |
| Online Access: | https://doi.org/10.1177/15589250221146548 |
| _version_ | 1797954638475952128 |
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| author | Yaolin Zhu Lu Zhao Xin Chen Yunhong Li Jinmei Wang |
| author_facet | Yaolin Zhu Lu Zhao Xin Chen Yunhong Li Jinmei Wang |
| author_sort | Yaolin Zhu |
| collection | DOAJ |
| description | There are invalid and redundant features in the texture feature extraction method of cashmere and wool fibers, which leads to the low recognition accuracy. In this paper, a novel texture feature selection method based on local binary pattern, the gray level co-occurrence matrix algorithm and chi-square test was proposed to sufficiently extract the effective features of these two fibers. Firstly, the collected images of cashmere and wool fibers are processed to obtain the clear texture images with background removed by pre-processing algorithm and local binary pattern. Then, the texture features are calculated by the gray level co-occurrence matrix, and the optimal 5-dimensional features are extracted by chi-square test to represent the texture information of cashmere and wool. Finally, the two fibers are automatically classified and recognized based on the support vector machine. The experimental results show that the proposed method obtained a high recognition accuracy with the percent of 94.39. It verifies that the method based on texture feature selection is effective to identify cashmere and wool fibers. |
| first_indexed | 2024-04-10T23:21:44Z |
| format | Article |
| id | doaj.art-22c9a3a3040744e097d912e75f8c01dc |
| institution | Directory Open Access Journal |
| issn | 1558-9250 |
| language | English |
| last_indexed | 2024-04-10T23:21:44Z |
| publishDate | 2023-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Journal of Engineered Fibers and Fabrics |
| spelling | doaj.art-22c9a3a3040744e097d912e75f8c01dc2023-01-12T14:04:31ZengSAGE PublishingJournal of Engineered Fibers and Fabrics1558-92502023-01-011810.1177/15589250221146548Identification of cashmere and wool based on LBP and GLCM texture feature selectionYaolin Zhu0Lu Zhao1Xin Chen2Yunhong Li3Jinmei Wang4School of Electronics and Information, Xi’an Polytechnic University, Xi’an, ChinaSchool of Electronics and Information, Xi’an Polytechnic University, Xi’an, ChinaSchool of Electronics and Information, Xi’an Polytechnic University, Xi’an, ChinaSchool of Electronics and Information, Xi’an Polytechnic University, Xi’an, ChinaSchool of Textile science and Engineering, Xi’an Polytechnic University, Xi’an, ChinaThere are invalid and redundant features in the texture feature extraction method of cashmere and wool fibers, which leads to the low recognition accuracy. In this paper, a novel texture feature selection method based on local binary pattern, the gray level co-occurrence matrix algorithm and chi-square test was proposed to sufficiently extract the effective features of these two fibers. Firstly, the collected images of cashmere and wool fibers are processed to obtain the clear texture images with background removed by pre-processing algorithm and local binary pattern. Then, the texture features are calculated by the gray level co-occurrence matrix, and the optimal 5-dimensional features are extracted by chi-square test to represent the texture information of cashmere and wool. Finally, the two fibers are automatically classified and recognized based on the support vector machine. The experimental results show that the proposed method obtained a high recognition accuracy with the percent of 94.39. It verifies that the method based on texture feature selection is effective to identify cashmere and wool fibers.https://doi.org/10.1177/15589250221146548 |
| spellingShingle | Yaolin Zhu Lu Zhao Xin Chen Yunhong Li Jinmei Wang Identification of cashmere and wool based on LBP and GLCM texture feature selection Journal of Engineered Fibers and Fabrics |
| title | Identification of cashmere and wool based on LBP and GLCM texture feature selection |
| title_full | Identification of cashmere and wool based on LBP and GLCM texture feature selection |
| title_fullStr | Identification of cashmere and wool based on LBP and GLCM texture feature selection |
| title_full_unstemmed | Identification of cashmere and wool based on LBP and GLCM texture feature selection |
| title_short | Identification of cashmere and wool based on LBP and GLCM texture feature selection |
| title_sort | identification of cashmere and wool based on lbp and glcm texture feature selection |
| url | https://doi.org/10.1177/15589250221146548 |
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