Revista UIS Ingenierías

Vol. 21 No. 3 (2022): Revista UIS Ingenierías
Articles

Spectral clustering and fuzzy similarity measure for images segmentation

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  • Juan Pablo Rodríguez-Fernández,
  • Pablo Lizarazo-Chilamá,
  • Elena Muñoz-España,
  • Juan Fernando Flórez-Marulanda
Juan Pablo Rodríguez-Fernández
Universidad del Cauca
Pablo Lizarazo-Chilamá
Universidad del Cauca
Elena Muñoz-España
Universidad del Cauca
Juan Fernando Flórez-Marulanda
Universidad del Cauca
DOI: https://doi.org/10.18273/revuin.v21n3-2022002

Published 2022-06-22

Keywords

  • fuzzy similarity measure,
  • image segmentation,
  • spectral clustering,
  • superpixels,
  • computational load,
  • computational cost
    • ...More
    Less

How to Cite

Rodríguez-Fernández, J. P., Lizarazo-Chilamá, P., Muñoz-España, E., & Flórez-Marulanda, J. F. (2022). Spectral clustering and fuzzy similarity measure for images segmentation. Revista UIS Ingenierías, 21(3), 9–20. https://doi.org/10.18273/revuin.v21n3-2022002

Copyright (c) 2022 Revista UIS Ingenierías

Creative Commons License

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Abstract

In image segmentation algorithms using spectral clustering, due to the size of the images, the computational load for the construction of the similarity matrix and the solution to the eigenvalue problem for the Laplacian matrix is high. Furthermore, the Gaussian kernel similarity measure is the most used, but it presents problems with irregular data distributions. This work proposes to perform a pre-segmentation or decimation by superpixels with the Simple Linear Iterative Clustering algorithm to reduce the computational cost, and to build the similarity matrix with a fuzzy measure based on the Fuzzy C-Means classifier, providing the algorithm a greater robustness against images with complex distributions and by spectral clustering the final segmentation is determined. Experimentally, it was found that the proposed approach obtains adequate segmentations, good clustering results and a comparable precision with respect to five algorithms; measuring performance under four determined validation metrics.

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