Micro Image Classification of 19 High-value Hardwood Species Based on Texture Feature Fusion
For classification of wood species with similar microstructure, 19 high-value hardwood species of Papilionaceae, Ebenaceae, and Caesalpiniaceae were used as experimental objects. Images of transverse sections, radial sections, and tangential sections were collected by Micro CT. Local binary patterns...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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North Carolina State University
2023-03-01
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| Series: | BioResources |
| Subjects: | |
| Online Access: | https://ojs.cnr.ncsu.edu/index.php/BRJ/article/view/22397 |
| _version_ | 1827916002428452864 |
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| author | Xiaoxia Yang Hailan Jiang Lixin Ma Wenhu Yang Xiaohan Zhao Cuiping Hu Zhedong Ge |
| author_facet | Xiaoxia Yang Hailan Jiang Lixin Ma Wenhu Yang Xiaohan Zhao Cuiping Hu Zhedong Ge |
| author_sort | Xiaoxia Yang |
| collection | DOAJ |
| description | For classification of wood species with similar microstructure, 19 high-value hardwood species of Papilionaceae, Ebenaceae, and Caesalpiniaceae were used as experimental objects. Images of transverse sections, radial sections, and tangential sections were collected by Micro CT. Local binary patterns (LBP) are often used for feature extraction. LBP deformed forms such as uniform LBP, rotation-invariant LBP, and rotation-invariant uniform LBP were fused with Gray-Level Co-Occurrence Matrix (GLCM) to form three fusion features. The fusion features were combined with support vector machine (SVM) or BP neural network to realize wood classification. The texture feature fusion method was found to be better than the single feature classification. Among them, the effect of uniform LBP and GLCM fusion was the best, and the classification accuracy combined with SVM was the highest. The evaluation of the classification of 19 kinds of hardwood mainly depended on transverse sections, and its accuracy was higher than that of the radial and tangential sections. Therefore, the classification results of transverse sections should be taken as the main evaluation basis for the classification of the 19 high-value hardwood species. |
| first_indexed | 2024-03-13T03:07:45Z |
| format | Article |
| id | doaj.art-411e0c05f02042dc8989c0d7c122ff0e |
| institution | Directory Open Access Journal |
| issn | 1930-2126 |
| language | English |
| last_indexed | 2024-03-13T03:07:45Z |
| publishDate | 2023-03-01 |
| publisher | North Carolina State University |
| record_format | Article |
| series | BioResources |
| spelling | doaj.art-411e0c05f02042dc8989c0d7c122ff0e2023-06-26T18:56:10ZengNorth Carolina State UniversityBioResources1930-21262023-03-0118233733386403Micro Image Classification of 19 High-value Hardwood Species Based on Texture Feature FusionXiaoxia Yang0Hailan Jiang1Lixin Ma2Wenhu Yang3Xiaohan Zhao4Cuiping Hu5Zhedong Ge6School of New Generation Information Technology Industry, Shandong PolytechnicSchool of New Generation Information Technology Industry, Shandong PolytechnicSchool of New Generation Information Technology Industry, Shandong PolytechnicSchool of New Generation Information Technology Industry, Shandong PolytechnicSchool of New Generation Information Technology Industry, Shandong PolytechnicLinshu County Garden Sanitation Guarantee Service Center, Linyi, Shandong 276700 ChinaSchool of Information and Electrical Engineering, Shandong Jianzhu UniversityFor classification of wood species with similar microstructure, 19 high-value hardwood species of Papilionaceae, Ebenaceae, and Caesalpiniaceae were used as experimental objects. Images of transverse sections, radial sections, and tangential sections were collected by Micro CT. Local binary patterns (LBP) are often used for feature extraction. LBP deformed forms such as uniform LBP, rotation-invariant LBP, and rotation-invariant uniform LBP were fused with Gray-Level Co-Occurrence Matrix (GLCM) to form three fusion features. The fusion features were combined with support vector machine (SVM) or BP neural network to realize wood classification. The texture feature fusion method was found to be better than the single feature classification. Among them, the effect of uniform LBP and GLCM fusion was the best, and the classification accuracy combined with SVM was the highest. The evaluation of the classification of 19 kinds of hardwood mainly depended on transverse sections, and its accuracy was higher than that of the radial and tangential sections. Therefore, the classification results of transverse sections should be taken as the main evaluation basis for the classification of the 19 high-value hardwood species.https://ojs.cnr.ncsu.edu/index.php/BRJ/article/view/22397high-value hardwoodfeature fusiontexture featureimage classification |
| spellingShingle | Xiaoxia Yang Hailan Jiang Lixin Ma Wenhu Yang Xiaohan Zhao Cuiping Hu Zhedong Ge Micro Image Classification of 19 High-value Hardwood Species Based on Texture Feature Fusion BioResources high-value hardwood feature fusion texture feature image classification |
| title | Micro Image Classification of 19 High-value Hardwood Species Based on Texture Feature Fusion |
| title_full | Micro Image Classification of 19 High-value Hardwood Species Based on Texture Feature Fusion |
| title_fullStr | Micro Image Classification of 19 High-value Hardwood Species Based on Texture Feature Fusion |
| title_full_unstemmed | Micro Image Classification of 19 High-value Hardwood Species Based on Texture Feature Fusion |
| title_short | Micro Image Classification of 19 High-value Hardwood Species Based on Texture Feature Fusion |
| title_sort | micro image classification of 19 high value hardwood species based on texture feature fusion |
| topic | high-value hardwood feature fusion texture feature image classification |
| url | https://ojs.cnr.ncsu.edu/index.php/BRJ/article/view/22397 |
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