Unsupervised Image Segmentation based Graph Clustering Methods
Unsupervised Image Segmentation based Graph Clustering Methods
复制标题
基于无监督图像分割的图聚类方法
DOI：
10.13053/cys-24-3-3059
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Fethi Guerdelli
中科院分区：
文献类型：
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作者：
Islem Gammoudi;M. Mahjoub;Fethi Guerdelli
基于图分割的图像分割是近年来fifi智能和计算机视觉领域的研究课题。在这种情况下，我们使用图作为图像或表示的模型，然后应用一种标准或方法将其划分为子图，其中图部分包括系统地去除边以生成两个子图。在本文中，我们提出了几种基于图划分的图像分割算法。我们在数据集Brats和标准测试图像Lenna上测试了我们的算法。我们的结果是有希望的。
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.