Publicado 2022-06-22
Palabras clave
- agrupamiento espectral,
- medida de similitud difusa,
- segmentación de imágenes,
- superpíxeles,
- carga computacional
- disminución de costo computacional ...Más
Cómo citar
Derechos de autor 2022 Revista UIS Ingenierías
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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