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Unsupervised image segmentation evaluation based on feature extraction

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Abstract

Image segmentation is widely used in life. Generally speaking, the segmentation results are divided into good and bad quality, so it is very important to propose an effective method to evaluate the quality of image segmentation. This paper proposed a framework based on edge detection and feature extraction for evaluating the quality of image segmentation. The framework belongs to unsupervised evaluation, the operation is simple and easy to implement, and readers can add or subtract methods in the framework according to specific circumstances. To prove the effectiveness of the proposed framework, we tested on four different datasets. In addition, we compare the proposed framework with some classic and newer evaluation methods. Experimental results show that the proposed framework is suitable for many types of images, and its performance is better than some existing metrics.

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Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

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Acknowledgements

We would like to thank the associate editors and the reviewers for their valuable comments and suggestions. The authors also thank Shuai Wang for his generous help. This work was supported by National Key R&D Program of China (No:2022YFF0711700) and Open Fund Project of National Cryosphere Desert Data Center (2022).

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Authors and Affiliations

  1. School of Infomation Science and Engineering, Lanzhou University, Lanzhou, 730000, China

    Zhaobin Wang, Xinchao Liu & E. Wang

  2. National Cryosphere Desert Data Center, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, China

    Yaonan Zhang

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Wang, Z., Liu, X., Wang, E. et al. Unsupervised image segmentation evaluation based on feature extraction. Multimed Tools Appl 83, 4887–4913 (2024). https://doi.org/10.1007/s11042-023-15384-z

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