Research Area:  Machine Learning

Abstract:

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.

Keywords:  

Author(s) Name:  Zhaobin Wang, E. Wang & Ying Zhu

Publisher name:  Springer

DOI:  10.1007/s10462-020-09830-9

Volume Information:  volume 53, pages5637–5674 (2020)

Paper Link:   https://link.springer.com/article/10.1007/s10462-020-09830-9