Explored Normalized Cut With Random Walk Refining Term for Image Segmentation

The Normalized Cut (NCut) model is a popular graph-based model for image segmentation. But it suffers from the excessive normalization problem and weakens the small object and twig segmentation. In this paper, we propose an Explored Normalized Cut (ENCut) model that establishes a balance graph model by adopting a meaningful-loop and a k-step random walk, which reduces the energy of small salient region, so as to enhance the small object segmentation. To improve the twig segmentation, our ENCut model is further enhanced by a new Random Walk Refining Term (RWRT) that adds local attention to our model with the help of an un-supervising random walk. Finally, a move-making based strategy is developed to efficiently solve the ENCut model with RWRT. Experiments on three standard datasets indicate that our model can achieve state-of-the-art results among the NCut-based segmentation models.

Automated summary

This paper proposes an ENCUT model that establishes a balanced graph model by adopting a meaningful-loop and a k-step random walk to enhance small object segmentation. The model is further improved by adding a new RWRT that adds local attention to the segmentation of twigs. Experimental results show that the model achieves state-of-the-art performance among NCut-based segmentation models.