Generative Image Segmentation Using Random Walks with Restart

160 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.

Reviving Iterative Training with Mask Guidance for Interactive Segmentation (2021, background): “Scribbles are used in many early works [5, 15, 16, 17, 26].”
Fast Global Image Smoothing Based on Weighted Least Squares (2014, background): “The separable global smoother is extended into a sparse data interpolation scenario, where the input data is sparse, such as image colorization [13], recoloring [9], and interactive image segmentation [33].”
Nonparametric higher-order learning for interactive segmentation (2010, methodology): “Specially it can be interpreted as a variant of Random Walks with Restart (RWR) [14][12] with the different criteria for deciding the restarting probability of each node in the graph G.”
Interactive Image Segmentation via Backpropagating Refinement Scheme (2019, methodology): “[21] perform the random walk with restart simulation to compute affinities between pixels.”
FocusCut: Diving into a Focus View in Interactive Segmentation (2022, methodology): “[22] improve it by introducing a restart simulation.”
Interactive Image Segmentation With First Click Attention (2020, methodology): “[26] improved the algorithm of random walk which is proposed in [18] with restart.”