Systematic review of image segmentation using complex networks

TL;DR

This paper systematically reviews various image segmentation techniques that utilize complex networks, highlighting their classifications, applications, and the integration of graph theory and hybrid methods for improved analysis.

Contribution

It provides a comprehensive classification and analysis of complex network-based image segmentation methods, emphasizing recent advances and hybrid approaches.

Findings

Graph theory enhances segmentation accuracy

Hybrid methods improve robustness and precision

Community detection aids in complex image analysis

Abstract

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.