Unsupervised Image Segmentation based Graph Clustering Methods

Authors

  • Islem Gammoudi Université de Sousse, Ecole Nationale d’Ingénieurs de Sousse, LATIS; Université de Tunis El Manar, Faculté des Sciences Mathématiques
  • Mohamed Ali Mahjoub Université de Sousse, Ecole Nationale d’Ingénieurs de Sousse, LATIS
  • Fethi Guerdelli Higher Colleges of Technologies Dubai men college, Dubai

DOI:

https://doi.org/10.13053/cys-24-3-3059

Keywords:

Image segmentation, graph partitioning, dataset (BRATS)

Abstract

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.

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Published

2020-09-29

Issue

Vol. 24 No. 3 (2020): 24(3) 2020

Section

Articles

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