A Robust Fuzzy Local Information C-Means Clustering Algorithm

By Stelios Krinidis, Vassilios Chatzis. IEEE Transactions on Image Processing, 2010

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Superpixel-Based Fast Fuzzy C-Means Clustering for Color Image Segmentation (2019, methodology): “As KWFLICM, Liu’s algorithm, and the proposed SFFCM employ adaptive local spatial information to improve segmentation results, they obtain larger S and SA than those comparative algorithms that employ local spatial information in a window of fixed size.”
Fuzzy C-Means Clustering With Local Information and Kernel Metric for Image Segmentation (2013, positive): “A lot of clusteringbased methods have been proposed for image segmentation [1]–[5].”
Significantly Fast and Robust Fuzzy C-Means Clustering Algorithm Based on Morphological Reconstruction and Membership Filtering (2018, methodology): “In [22], FLICM will be equal to FCM, ifGki is removed.”
Image segmentation by generalized hierarchical fuzzy C-means algorithm (2015, methodology): “This is expected that no spatial constraints are taken into account in GGFCM. FCM S, FLICM and HMRF-FCM obtain better results, but they are still sensitive to heavy noise.”