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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Bibliographic Details
Published in:IEEE signal processing letters Vol. 28; pp. 897 - 901
Main Authors: Guan, Junyi, Li, Sheng, He, Xiongxiong, Chen, Jiajia
Format: Journal Article
Language:English
Published: New York IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
ISSN:1070-9908, 1558-2361
Online Access:Get full text
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Summary: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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ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2021.3072794