Peak-Graph-Based Fast Density Peak Clustering for Image Segmentation
Fuzzy c-means (FCM) algorithm as a traditional clustering algorithm for image segmentation cannot effectively preserve local spatial information of pixels, which leads to poor segmentation results with inconsistent regions. For the remedy, superpixel technologies are applied, but spatial information...
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| Published in: | IEEE signal processing letters Vol. 28; pp. 897 - 901 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1070-9908, 1558-2361 |
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| Abstract | Fuzzy c-means (FCM) algorithm as a traditional clustering algorithm for image segmentation cannot effectively preserve local spatial information of pixels, which leads to poor segmentation results with inconsistent regions. For the remedy, superpixel technologies are applied, but spatial information preservation highly relies on the quality of superpixels. Density peak clustering algorithm (DPC) can reconstruct spatial information of arbitrary-shaped clusters, but its high time complexity <inline-formula><tex-math notation="LaTeX">O(n^2)</tex-math></inline-formula> and unrobust allocation strategy decrease its applicability for image segmentation. Herein, a fast density peak clustering method (PGDPC) based on the kNN distance matrix of data with time complexity <inline-formula><tex-math notation="LaTeX">O(nlog(n))</tex-math></inline-formula> is proposed. By using the peak-graph-based allocation strategy, PGDPC is more robust in the reconstruction of spatial information of various complex-shaped clusters, so it can rapidly and accurately segment images into high-consistent segmentation regions. Experiments on synthetic datasets, real and Wireless Capsule Endoscopy (WCE) images demonstrate that PGDPC as a fast and robust clustering algorithm is applicable to image segmentation. |
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| AbstractList | Fuzzy c-means (FCM) algorithm as a traditional clustering algorithm for image segmentation cannot effectively preserve local spatial information of pixels, which leads to poor segmentation results with inconsistent regions. For the remedy, superpixel technologies are applied, but spatial information preservation highly relies on the quality of superpixels. Density peak clustering algorithm (DPC) can reconstruct spatial information of arbitrary-shaped clusters, but its high time complexity [Formula Omitted] and unrobust allocation strategy decrease its applicability for image segmentation. Herein, a fast density peak clustering method (PGDPC) based on the kNN distance matrix of data with time complexity [Formula Omitted] is proposed. By using the peak-graph-based allocation strategy, PGDPC is more robust in the reconstruction of spatial information of various complex-shaped clusters, so it can rapidly and accurately segment images into high-consistent segmentation regions. Experiments on synthetic datasets, real and Wireless Capsule Endoscopy (WCE) images demonstrate that PGDPC as a fast and robust clustering algorithm is applicable to image segmentation. Fuzzy c-means (FCM) algorithm as a traditional clustering algorithm for image segmentation cannot effectively preserve local spatial information of pixels, which leads to poor segmentation results with inconsistent regions. For the remedy, superpixel technologies are applied, but spatial information preservation highly relies on the quality of superpixels. Density peak clustering algorithm (DPC) can reconstruct spatial information of arbitrary-shaped clusters, but its high time complexity <inline-formula><tex-math notation="LaTeX">O(n^2)</tex-math></inline-formula> and unrobust allocation strategy decrease its applicability for image segmentation. Herein, a fast density peak clustering method (PGDPC) based on the kNN distance matrix of data with time complexity <inline-formula><tex-math notation="LaTeX">O(nlog(n))</tex-math></inline-formula> is proposed. By using the peak-graph-based allocation strategy, PGDPC is more robust in the reconstruction of spatial information of various complex-shaped clusters, so it can rapidly and accurately segment images into high-consistent segmentation regions. Experiments on synthetic datasets, real and Wireless Capsule Endoscopy (WCE) images demonstrate that PGDPC as a fast and robust clustering algorithm is applicable to image segmentation. |
| Author | Guan, Junyi He, Xiongxiong Li, Sheng Chen, Jiajia |
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| SubjectTerms | Algorithms Clustering Clustering algorithms Clustering methods Complexity Density density peak clustering Image reconstruction Image segmentation kNN peak-graph Resource management Robustness Signal processing algorithms Spatial data Vegetation |
| Title | Peak-Graph-Based Fast Density Peak Clustering for Image Segmentation |
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