Improved kernel density peaks clustering for plant image segmentation applications
In order to better solve the shortcomings of the k-means clustering method and density peaks clustering (DPC) method in agricultural image segmentation, this work proposes a method to divide points in a high-dimensional space, and a clustering method is obtained to divide crops and soil. In the proc...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2023-12-01
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| Series: | Journal of Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jisys-2022-0151 |
| _version_ | 1797405088562544640 |
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| author | Bi Jiaze Zhang Pingzhe Gao Yujia Dong Menglong Zhuang Yongzhi Liu Ao Zhang Wei Chen Yiqiong |
| author_facet | Bi Jiaze Zhang Pingzhe Gao Yujia Dong Menglong Zhuang Yongzhi Liu Ao Zhang Wei Chen Yiqiong |
| author_sort | Bi Jiaze |
| collection | DOAJ |
| description | In order to better solve the shortcomings of the k-means clustering method and density peaks clustering (DPC) method in agricultural image segmentation, this work proposes a method to divide points in a high-dimensional space, and a clustering method is obtained to divide crops and soil. In the process of assigning points in the DPC method, if a point is divided incorrectly, a series of points may be assigned to a cluster that is not related to it. In response to this problem, this study uses the decision graph to select the centroids, and uses Gaussian kernel to map the data to the high-dimensional space, each centroid searches for the most relevant points in the high-dimensional space until a temporary boundary point is found to stop the first assignment strategy, and then the points that are not clustered are assigned to the correct cluster to complete the clustering. The experimental results show that the proposed method has a better clustering effect through experiments on multiple artificial datasets and UCI datasets, compared with other clustering methods, and finally applied to plant image segmentation. |
| first_indexed | 2024-03-09T03:05:39Z |
| format | Article |
| id | doaj.art-1158a2aa693c40f4afc4819ddbb7ec02 |
| institution | Directory Open Access Journal |
| issn | 2191-026X |
| language | English |
| last_indexed | 2024-03-09T03:05:39Z |
| publishDate | 2023-12-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Intelligent Systems |
| spelling | doaj.art-1158a2aa693c40f4afc4819ddbb7ec022023-12-04T07:59:47ZengDe GruyterJournal of Intelligent Systems2191-026X2023-12-01321p. 40141310.1515/jisys-2022-0151Improved kernel density peaks clustering for plant image segmentation applicationsBi Jiaze0Zhang Pingzhe1Gao Yujia2Dong Menglong3Zhuang Yongzhi4Liu Ao5Zhang Wei6Chen Yiqiong7School of Information and Computer, Anhui Agricultural University, Hefei, 230036, ChinaSchool of Information and Computer, Anhui Agricultural University, Hefei, 230036, ChinaSchool of Information and Computer, Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, 230036, ChinaSchool of Information and Computer, Anhui Agricultural University, Hefei, 230036, ChinaSchool of Information and Computer, Anhui Agricultural University, Hefei, 230036, ChinaSchool of Information and Computer, Anhui Agricultural University, Hefei, 230036, ChinaSchool of Information and Computer, Anhui Agricultural University, Hefei, 230036, ChinaSchool of Information and Computer, Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, 230036, ChinaIn order to better solve the shortcomings of the k-means clustering method and density peaks clustering (DPC) method in agricultural image segmentation, this work proposes a method to divide points in a high-dimensional space, and a clustering method is obtained to divide crops and soil. In the process of assigning points in the DPC method, if a point is divided incorrectly, a series of points may be assigned to a cluster that is not related to it. In response to this problem, this study uses the decision graph to select the centroids, and uses Gaussian kernel to map the data to the high-dimensional space, each centroid searches for the most relevant points in the high-dimensional space until a temporary boundary point is found to stop the first assignment strategy, and then the points that are not clustered are assigned to the correct cluster to complete the clustering. The experimental results show that the proposed method has a better clustering effect through experiments on multiple artificial datasets and UCI datasets, compared with other clustering methods, and finally applied to plant image segmentation.https://doi.org/10.1515/jisys-2022-0151agricultural image segmentationclusterdensity peakkernel |
| spellingShingle | Bi Jiaze Zhang Pingzhe Gao Yujia Dong Menglong Zhuang Yongzhi Liu Ao Zhang Wei Chen Yiqiong Improved kernel density peaks clustering for plant image segmentation applications Journal of Intelligent Systems agricultural image segmentation cluster density peak kernel |
| title | Improved kernel density peaks clustering for plant image segmentation applications |
| title_full | Improved kernel density peaks clustering for plant image segmentation applications |
| title_fullStr | Improved kernel density peaks clustering for plant image segmentation applications |
| title_full_unstemmed | Improved kernel density peaks clustering for plant image segmentation applications |
| title_short | Improved kernel density peaks clustering for plant image segmentation applications |
| title_sort | improved kernel density peaks clustering for plant image segmentation applications |
| topic | agricultural image segmentation cluster density peak kernel |
| url | https://doi.org/10.1515/jisys-2022-0151 |
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