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Journal of Fuzzy Logic and Modeling in Engineering

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ISSN (Print): 2666-2949
ISSN (Online): 2666-2957

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Research Article

A New Cutset-type Kernelled Possibilistic C-means Clustering Segmentation Algorithm Based on SLIC Super-pixels

Author(s): Jiulun Fan, Haiyan Yu*, Yang Yan and Mengfei Gao

Volume 1, Issue 1, 2022

Published on: 05 January, 2021

Article ID: e010621189941 Pages: 17

DOI: 10.2174/2666294901666210105141957

Abstract

Background: The kernelled possibilistic C-means clustering algorithm (KPCM) can effectively cluster hyper-sphere data with noise and outliers by introducing the kernelled method to the possibilistic C-means clustering (PCM) algorithm. However, the KPCM still suffers from the same coincident clustering problem as the PCM algorithm due to the lack of between-class relationships.

Introduction: This paper introduces the cut-set theory into the KPCM and proposes a novel cutsettype kernelled possibilistic C-means clustering (C-KPCM) algorithm to solve the coincident clustering problem of the KPCM.

Methods: In the C-KPCM, the memberships of some data samples in a cluster core which is generated by the cut-set theory are selected. Then the values of the selected memberships are modified in the iterative process to introduce the between-class relationship in the KPCM. Simultaneously an adaptive method of estimating the cut-set threshold is also given by averaging inter-class distances. Additionally, a cutset-type kernelled possibilistic C-means clustering segmentation algorithm based on the SLIC super-pixels (SS-C-KPCM) is also proposed to improve the segmentation quality and efficiency of the color images

Results: Several experimental results on artificial data sets and image segmentation simulation results prove the excellent performance of the proposed algorithms in this paper.

Conclusion: The proposed C-KPCM can overcome the coincident clustering problem of the KPCM algorithm and the proposed SS-C-KPCM can reduce the misclassification points and improve the color segmentation performance.

Keywords: Possibilistic clustering, kernelled possibilistic clustering, cut-set theory, super-pixels, image segmentation, Cmeans clustering.

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