Abstract
The traditional FCM algorithm is developed on the basis of classical fuzzy theory, though the classical fuzzy theory has its own limitations. The lack of expressive ability of uncertain information makes it hard for FCM algorithm to handle clustered boundary pixels and outliers. This paper proposes a Neutrosophic C-means Clustering with Local Information and Noise Distance-based Kernel Metric for Image Segmentation (NKWNLICM). The concept of local fuzzy information and noise distance in the Neutrosophic C-means Clustering Algorithm (NCM) is introduced in the paper. The algorithm improves the efficiency by leaving out parameter setting for different noises when segmenting pictures, and it also improves the robustness. Simulation results show that the algorithm has better segmentation results for noisy images.
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Acknowledgments
This work has been supported in part by the National Natural Science Foundation of China (Grant No. 61773220, 61502206), the Nature Science Foundation of Jiangsu Province under Grant (No. BK20150523)
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Lu, Z., Qiu, Y., Zhan, T. (2018). Neutrosophic C-means Clustering with Local Information and Noise Distance-Based Kernel Metric Image Segmentation. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11164. Springer, Cham. https://doi.org/10.1007/978-3-030-00776-8_16
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DOI: https://doi.org/10.1007/978-3-030-00776-8_16
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