Fuzzy C-Means Clustering With Local Information and Kernel Metric for Image Segmentation

By Maoguo Gong, Yan Liang, Jiao Shi, Wenping Ma, Jingjing Ma. IEEE Transactions on Image Processing, 2013

624 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Self-Supervised Representation Learning for Remote Sensing Image Change Detection Based on Temporal Prediction (2020, methodology): “Analyzing DI can be viewed as an image segmentation task [39].”
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.”
A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images (2018, methodology): “Because our proposed method is unsupervised, for fair comparison, we use the unsupervised image segmentation algorithm [56] to classify the two images.”
Significantly Fast and Robust Fuzzy C-Means Clustering Algorithm Based on Morphological Reconstruction and Membership Filtering (2018, methodology): “…by introducing a kernel metric to FLICM, and employing a tradeoff weighted fuzzy factor to control adaptively the local spatial relationship, Gong et al. [24] proposed a novel FCM with local information and kernel metric (KWFLICM) to enhance the robustness of FLICM to noise and outliers.”