Semi Supervised Image Segmentation Based on Markov Random Field and Kernel K-means
Xianfeng Liu, Zhiliang Gao, Ming Chen, Dandan Luo · Journal of Physics Conference Series · 2020
Abstract Semi supervised image segmentation uses a small amount of supervised information to improve the performance of image segmentation. In the first part of this paper, we construct a submodular regularization term based on Markov random field (MRF). The regularization term is combined with kernel k-means (KKM), and a semi supervised kernel k-means algorithm (SKKM) is designed based on graph cut technique. In the second part of this paper, we combine SKKM with smooth regularizer to get MRF & SKKM. The experimental results show that, compared with the original SKKM, the segmentation results of MRF & SKKM achieve better results in common evaluation indicators; moreover, compared with the binary image of segmentation results, MRF & SKKM has higher and smoother edge fiting.