Iterative image segmentation with feature driven heuristic four-color labeling
作者：
Highlights：
• A heuristic four color labeling method is proposed to give robust initial foul-phase partition for Multiphase Multiple Piecewise Constant (MMPC) model.
• A regional adjacency cracking method is proposed to remove unnecessary adjacency constraints which impede the four color labeling.
• Compared with the random four color labeling, the color map of heuristic coloring shows better consistency for the homogenous regions.
• The heuristic four color labeling based approach reaches the good or even better segmentation with fewer iterations.
摘要
•A heuristic four color labeling method is proposed to give robust initial foul-phase partition for Multiphase Multiple Piecewise Constant (MMPC) model.•A regional adjacency cracking method is proposed to remove unnecessary adjacency constraints which impede the four color labeling.•Compared with the random four color labeling, the color map of heuristic coloring shows better consistency for the homogenous regions.•The heuristic four color labeling based approach reaches the good or even better segmentation with fewer iterations.
论文关键词：Image segmentation,Mean shift,Four color theorem,Affinity propagation clustering
论文评审过程：Received 1 August 2016, Revised 30 September 2017, Accepted 16 October 2017, Available online 18 October 2017, Version of Record 1 November 2017.
论文官网地址：https://doi.org/10.1016/j.patcog.2017.10.023
