Entropy-like Divergence Based Kernel Fuzzy Clustering for Robust Image Segmentation

作者：

Highlights：

• An entropy-like divergence and its mercer kernel function are presented.

• A novel divergence-based kernel fuzzy clustering is proved to be convergent.

• A kernel weighted fuzzy clustering based on entropy-like divergence is constructed.

• Proposed algorithm outperforms existing KWFLICM and ILKFCM for image segmentation.

摘要

•An entropy-like divergence and its mercer kernel function are presented.•A novel divergence-based kernel fuzzy clustering is proved to be convergent.•A kernel weighted fuzzy clustering based on entropy-like divergence is constructed.•Proposed algorithm outperforms existing KWFLICM and ILKFCM for image segmentation.

论文关键词：Image segmentation,Entropy-like divergence-based kernel,Weighted factor,Fuzzy local information,Image with high noise

论文评审过程：Received 28 March 2020, Revised 13 November 2020, Accepted 14 November 2020, Available online 21 November 2020, Version of Record 10 February 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.114327