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A Robust Fuzzy Local Information C-Means Clustering Algorithm
Abstract
This paper presents a variation of fuzzy c-means (FCM) algorithm that provides image clustering. The proposed algorithm incorporates the local spatial information and gray level information in a novel fuzzy way. The new algorithm is called fuzzy local information C-Means (FLICM). FLICM can overcome the disadvantages of the known fuzzy c-means algorithms and at the same time enhances the clustering performance. The major characteristic of FLICM is the use of a fuzzy local (both spatial and gray level) similarity measure, aiming to guarantee noise insensitiveness and image detail preservation. Furthermore, the proposed algorithm is fully free of the empirically adjusted parameters (a, ¿(g), ¿(s), etc.) incorporated into all other fuzzy c-means algorithms proposed in the literature. Experiments performed on synthetic and real-world images show that FLICM algorithm is effective and efficient, providing robustness to noisy images.
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Cited by
Lei, T., Jia, X., Zhang, Y., He, L., Meng, H., & Nandi, A. K. (2018) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2018.2796074 · Cited by 482
Superpixel-Based Fast Fuzzy C-Means Clustering for Color Image Segmentation
Lei, T., Jia, X., Zhang, Y., Liu, S., Meng, H., & Nandi, A. K. (2019) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2018.2889018 · Cited by 369
Gong, M., Yang, H., & Zhang, P. (2017) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2017.05.001 · Cited by 226
Huang, L., & Lu, H. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.1799 · Cited by 10
Metadata
- Authors
- Krinidis, S., & Chatzis, V.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2010-05
- Volume
- 19
- Issue
- 5
- Pages
- 1328-1337
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2165012164
- Cited by
- 1,124
- References
- 0
- FWCI
- 32.99
- Type
- journal article
- Language
- en
- Open access
- closed
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