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Volume 1, Issue 1
  • ISSN: 2666-2949
  • E-ISSN: 2666-2957

Abstract

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.

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

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.

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

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.

© 2022 Bentham Science Publishers
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/content/journals/flme/10.2174/2666294901666210105141957
2022-04-01
2024-11-22

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/content/journals/flme/10.2174/2666294901666210105141957
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A New Cutset-type Kernelled Possibilistic C-means Clustering Segmentation Algorithm Based on SLIC Super-pixels
Journal of Fuzzy Logic and Modeling in Engineering 1, e010621189941 (2022); https://doi.org/10.2174/2666294901666210105141957
/content/journals/flme/10.2174/2666294901666210105141957
/content/journals/flme/10.2174/2666294901666210105141957
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  • Article Type:     Research Article
Keyword(s): C-means clustering; cut-set theory; image segmentation; kernelled possibilistic clustering; Possibilistic clustering; super-pixels

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