Research on Density Peak Clustering Color-Separation Algorithm Based on SOM Neural Network
Pattern images input to The knitting CAD system have a large number of different colors, and it is necessary to reduce the number of colors in the image by merging similar colors through color-separation algorithms. However, current pattern images usually have the problem of image degradation, which...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10829579/ |
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| _version_ | 1832576776392409088 |
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| author | Xin Ru Yanhao Wang Ran Chen Zhifa Zeng Laihu Peng Wei Tang Weimin Shi Hui Wang |
| author_facet | Xin Ru Yanhao Wang Ran Chen Zhifa Zeng Laihu Peng Wei Tang Weimin Shi Hui Wang |
| author_sort | Xin Ru |
| collection | DOAJ |
| description | Pattern images input to The knitting CAD system have a large number of different colors, and it is necessary to reduce the number of colors in the image by merging similar colors through color-separation algorithms. However, current pattern images usually have the problem of image degradation, which seriously affects the accuracy of color-separation algorithms. In addition, the traditional color-separation algorithm needs to rely on the manual setting of clustering parameters, which is very time-consuming and laborious. To solve these problems, In this paper, we propose a density peak clustering color-separation algorithm based on self-organizing mapping (SOM) neural network, which firstly uses enhanced super-resolution generative adversarial network (Real-ESRGAN) blind super-resolution reconstruction network to clarify the degraded image and obtain a high-resolution image with clear boundaries; secondly, we carry out the initial clustering through the SOM neural network to simplify the image information; and then we use an improved density peak clustering (DPC) algorithm to calculate clustering centers under the conditions of conforming to the perception of human eyes, and carry out secondary clustering on the image; finally, carry out image post-processing through the variegated spots merging algorithm based on the connected component analysis. The experimental results show that the algorithm proposed in this study can effectively deal with degraded pattern images, and the clustering effectiveness evaluation indices perform well. |
| format | Article |
| id | doaj-art-ab451f62be534250946b1d2210a90954 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-ab451f62be534250946b1d2210a909542025-01-31T00:02:02ZengIEEEIEEE Access2169-35362025-01-0113168111682310.1109/ACCESS.2025.352623310829579Research on Density Peak Clustering Color-Separation Algorithm Based on SOM Neural NetworkXin Ru0Yanhao Wang1https://orcid.org/0009-0000-8493-3593Ran Chen2Zhifa Zeng3https://orcid.org/0009-0009-1181-285XLaihu Peng4https://orcid.org/0000-0002-5932-104XWei Tang5https://orcid.org/0009-0003-0616-3548Weimin Shi6Hui Wang7https://orcid.org/0009-0005-0920-0919School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou, ChinaSchool of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou, ChinaSchool of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou, ChinaZhejiang Hengqiang Technology Company Ltd., Hangzhou, ChinaSchool of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou, ChinaZhejiang Hengqiang Technology Company Ltd., Hangzhou, ChinaSchool of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou, ChinaZhejiang Hengqiang Technology Company Ltd., Hangzhou, ChinaPattern images input to The knitting CAD system have a large number of different colors, and it is necessary to reduce the number of colors in the image by merging similar colors through color-separation algorithms. However, current pattern images usually have the problem of image degradation, which seriously affects the accuracy of color-separation algorithms. In addition, the traditional color-separation algorithm needs to rely on the manual setting of clustering parameters, which is very time-consuming and laborious. To solve these problems, In this paper, we propose a density peak clustering color-separation algorithm based on self-organizing mapping (SOM) neural network, which firstly uses enhanced super-resolution generative adversarial network (Real-ESRGAN) blind super-resolution reconstruction network to clarify the degraded image and obtain a high-resolution image with clear boundaries; secondly, we carry out the initial clustering through the SOM neural network to simplify the image information; and then we use an improved density peak clustering (DPC) algorithm to calculate clustering centers under the conditions of conforming to the perception of human eyes, and carry out secondary clustering on the image; finally, carry out image post-processing through the variegated spots merging algorithm based on the connected component analysis. The experimental results show that the algorithm proposed in this study can effectively deal with degraded pattern images, and the clustering effectiveness evaluation indices perform well.https://ieeexplore.ieee.org/document/10829579/Color-separation algorithmdensity peak clustering (DPC)knitting CADself-organizing mapping (SOM) neural networkblind super-resolution reconstruction network |
| spellingShingle | Xin Ru Yanhao Wang Ran Chen Zhifa Zeng Laihu Peng Wei Tang Weimin Shi Hui Wang Research on Density Peak Clustering Color-Separation Algorithm Based on SOM Neural Network IEEE Access Color-separation algorithm density peak clustering (DPC) knitting CAD self-organizing mapping (SOM) neural network blind super-resolution reconstruction network |
| title | Research on Density Peak Clustering Color-Separation Algorithm Based on SOM Neural Network |
| title_full | Research on Density Peak Clustering Color-Separation Algorithm Based on SOM Neural Network |
| title_fullStr | Research on Density Peak Clustering Color-Separation Algorithm Based on SOM Neural Network |
| title_full_unstemmed | Research on Density Peak Clustering Color-Separation Algorithm Based on SOM Neural Network |
| title_short | Research on Density Peak Clustering Color-Separation Algorithm Based on SOM Neural Network |
| title_sort | research on density peak clustering color separation algorithm based on som neural network |
| topic | Color-separation algorithm density peak clustering (DPC) knitting CAD self-organizing mapping (SOM) neural network blind super-resolution reconstruction network |
| url | https://ieeexplore.ieee.org/document/10829579/ |
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