Revista UIS Ingenierías

Vol. 21 Núm. 3 (2022): Revista UIS Ingenierías
Artículos

Segmentación de imágenes mediante agrupación espectral y medida de similaridad difusa

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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

Publicado 2022-06-22

Palabras clave

  • agrupamiento espectral,
  • medida de similitud difusa,
  • segmentación de imágenes,
  • superpíxeles,
  • carga computacional,
  • disminución de costo computacional
    • ...Más
    Menos

Cómo citar

Rodríguez-Fernández, J. P., Lizarazo-Chilamá, P., Muñoz-España, E., & Flórez-Marulanda, J. F. (2022). Segmentación de imágenes mediante agrupación espectral y medida de similaridad difusa. Revista UIS Ingenierías, 21(3), 9–20. https://doi.org/10.18273/revuin.v21n3-2022002

Derechos de autor 2022 Revista UIS Ingenierías

Creative Commons License

Esta obra está bajo una licencia internacional Creative Commons Atribución-SinDerivadas 4.0.

Resumen

En los algoritmos de segmentación de imágenes mediante agrupamiento espectral, debido al tamaño de las imágenes, la carga computacional para la construcción de la matriz de similitud y la solución al problema de valores propios para la matriz laplaciana son altos. Además, la medida de similitud más utilizada es el kernel gaussiano, el cual presenta problemas con distribuciones de datos irregulares. Este trabajo propone realizar una presegmentación o diezmado mediante superpíxeles con el algoritmo Simple Linear Iterative Clustering, para disminuir el costo computacional y construir la matriz de similaridad con una medida difusa basada en el clasificador Fuzzy C-Means, que proporciona al algoritmo una mayor robustez frente a imágenes con distribuciones complejas; mediante agrupamiento espectral se determina la segmentación final. Experimentalmente, se comprobó que el enfoque propuesto obtiene segmentaciones adecuadas, buenos resultados de agrupamiento y una precisión comparable respecto a cinco algoritmos, midiendo el desempeño bajo cuatro métricas de validación.

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