Image segmentation is a prerequisite for image processing. There are many methods for image segmentation, and as a result, a great number of methods for evaluating segmentation results have also been proposed. How to effectively evaluate the quality of image segmentation is very important. In this paper, the existing image segmentation quality evaluation methods are summarized, mainly including unsupervised methods and supervised methods. Based on hot issues, the application of metrics in natural, medical and remote sensing image evaluation is further outlined. In addition, an experimental comparison for some methods were carried out and the effectiveness of these methods was ranked. At the same time, the effectiveness of classical metrics for remote sensing and medical image evaluation is also verified.

中文翻译：

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图像分割评估：方法综述

图像分割是图像处理的前提。图像分割的方法有很多，因此也提出了大量的评估分割结果的方法。如何有效地评价图像分割的质量非常重要。本文总结了现有的图像分割质量评价方法，主要包括无监督方法和有监督方法。基于热点问题，进一步概述了度量在自然、医学和遥感图像评估中的应用。此外，还对一些方法进行了实验比较，并对这些方法的有效性进行了排序。同时，也验证了经典度量在遥感和医学图像评估中的有效性。