This review presents various image segmentation methods using complex networks. Image segmentation is one of the important steps in image analysis as it helps analyze and understand complex images. At first, it has been tried to classify complex networks based on how it being used in image segmentation. In computer vision and image processing applications, image segmentation is essential for analyzing complex images with irregular shapes, textures, or overlapping boundaries. Advanced algorithms make use of machine learning, clustering, edge detection, and region-growing techniques. Graph theory principles combined with community detection-based methods allow for more precise analysis and interpretation of complex images. Hybrid approaches combine multiple techniques for comprehensive, robust segmentation, improving results in computer vision and image processing tasks.

翻译：本综述介绍了多种基于复杂网络的图像分割方法。图像分割是图像分析中的关键步骤之一，有助于分析和理解复杂的图像。首先，本文尝试根据复杂网络在图像分割中的应用方式对其进行分类。在计算机视觉和图像处理应用中，图像分割对于分析具有不规则形状、纹理或重叠边界的复杂图像至关重要。先进的算法利用了机器学习、聚类、边缘检测和区域生长技术。基于图论原理与社区检测相结合的方法，能够对复杂图像进行更精确的分析和解读。混合方法结合多种技术实现全面、稳健的分割，从而提升计算机视觉和图像处理任务的效果。