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Quantum-inspired ant lion-optimized hybrid fuzzy c-means method for fuzzy clustering and image segmentation

  • Data analytics and machine learning
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Abstract

In the research field of computer vision and image recognition, images need to be preprocessed, and image segmentation is an essential method of image preprocessing. Researchers usually use fuzzy clustering algorithms for image preprocessing. The fuzzy c-means (FCM) method is easy to implement, so it is the most widely used and more successful among many fuzzy clustering algorithms. However, the algorithm is highly sensitive to randomly initialized cluster centers, and the final result is highly likely to be a local optimal value. Therefore, we use quantum-inspired ant lion optimizer (QALO) to optimize FCM clustering method and propose an efficient hybrid clustering algorithm called QALOFCM. The UCI dataset is used as an experimental dataset to testify the clustering effect of the method and compare and analyze the clustering results with other well-known clustering algorithms. The algorithm is designed to perform image segmentation experiments on Simulated Brain Database. The final comparative experiment can draw a conclusion that the hybrid method can not only be effectively used for fuzzy clustering, but also can be effectively applied in image segmentation.

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Data Availability

The datasets analyzed during the current study are available in the Machine Learning Repository, archive.ics.uci.edu, and Simulated Brain Database, brainweb.bic.mni.mcgill.ca.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (61972438) and Key Research and Development Projects in Anhui Province (202004a05020002).

Funding

This study was funded by the National Natural Science Foundation of China (61972438) and Key Research and Development Projects in Anhui Province (202004a05020002).

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Authors and Affiliations

  1. School of Computer and Information, Anhui Normal University, Wuhu, 241003, China

    Junwen Chen, Xuemei Qi, Fulong Chen & Guihua Cheng

  2. Anhui Provincial Key Laboratory of Network and Information Security, Wuhu, 241003, China

    Junwen Chen, Xuemei Qi, Fulong Chen & Guihua Cheng

Authors
  1. Junwen Chen
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  2. Xuemei Qi
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  3. Fulong Chen
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  4. Guihua Cheng
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Contributions

All authors contributed to the study conception and design. J. Chen and X. Qi conceived the presented idea. J. Chen carried out the experiment and wrote the manuscript with support from X. Qi, F. Chen, and G. Cheng. F. Chen and G. Cheng helped in supervising the findings of this work. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Fulong Chen.

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Chen, J., Qi, X., Chen, F. et al. Quantum-inspired ant lion-optimized hybrid fuzzy c-means method for fuzzy clustering and image segmentation. Soft Comput 25, 15021–15034 (2021). https://doi.org/10.1007/s00500-021-06391-z

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