Comparative study of distance measures for the fuzzy C-means and K-means non-supervised methods applied to image segmentation


Abstract:

Recent studies have revealed that the performance of the FCM and K-means is completely related to the distance measures. However, the literature does not provide evidence that the distance used for data-clustering is useful for image segmentation. Therefore, a comparative study of the performance of different distance measures applied to image segmentation, using the mentioned clustering methods is proposed in this work. The selection of the distance measures was based on a literature study of their benefits. As a consequence, the selected distances to be tested are Euclidean, Manhattan, Canberra, and Spearman. Since our principal goal is to compare the effectiveness of the distance, the experiment had been evaluated according to two centroids selected by the user. According to primary results, the best-rated distance employed for image segmentation is the Canberra distance.

Año de publicación:

2020

Keywords:

  • image segmentation
  • Non-supervised algorithms
  • Clustering

Fuente:

scopusscopus

Tipo de documento:

Conference Object

Estado:

Acceso restringido

Áreas de conocimiento:

  • Visión por computadora
  • Lógica difusa
  • Ciencias de la computación

Áreas temáticas:

  • Métodos informáticos especiales
  • Ciencias de la computación
  • Funcionamiento de bibliotecas y archivos