Resumen

GAMMOUDI, Islem; MAHJOUB, Mohamed Ali  y  GUERDELLI, Fethi. Unsupervised Image Segmentation based Graph Clustering Methods. Comp. y Sist. [online]. 2020, vol.24, n.3, pp.969-987.  Epub 09-Jun-2021. ISSN 2007-9737.  https://doi.org/10.13053/cys-24-3-3059.

Image Segmentation by Graph Partitioning is the subject of several research areas, recently, in the field of artificial intelligence and computer vision. In this context, we use graphs as models of images or representations, then we apply a criterion or methodology to divide it into sub-graphs where a graph section consists on systematically removing the edges to generate two sub-graphs. In this paper, we present Several image segmentation algorithms formulated from the graph partition. We test our algorithms on the dataset BRATS and standard test image Lenna. Our result are promising.

Palabras llave : Image segmentation; graph partitioning; dataset (BRATS).

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