Random Walks for Image Segmentation

By Leo J. Grady. 2006

2,797 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

Citations in the corpus

SimpleClick: Interactive Image Segmentation with Simple Vision Transformers (2022, background): “Early works [5,14,16,37] tackle this problem by optimizing a graph defined over image pixels.”
FocalClick: Towards Practical Interactive Image Segmentation (2022, background): “It enables users to annotate masks conveniently using simple interactions like scribbles [1, 11, 21], boxes [20, 34, 40], or clicks [5, 18, 27, 35, 36, 44].”
Reviving Iterative Training with Mask Guidance for Interactive Segmentation (2021, methodology): “Scribbles are used in many early works [5, 15, 16, 17, 26].”
Iteratively Trained Interactive Segmentation (2018, background): “Graph cut [3] >20 >20 Geodesic matting [1] >20 >20 Random walker [13] 16.”
Deep Extreme Cut: From Extreme Points to Object Segmentation (2017, neutral): “6 Random walker [9] 16.”
Extreme Clicking for Efficient Object Annotation (2017, methodology): “To reduce annotation time many interactive segmentation techniques have been proposed, which require the user to input either a bounding box around the object [42, 51, 71], or scribbles [3, 16, 24, 26, 27, 42, 50, 64, 65, 72], or clicks [31, 69].”
Deep Interactive Object Selection (2016, methodology): “Strokebased methods are popular, and use a number of underlying algorithms, including normalized cuts [22], graph cut [2, 11, 26], geodesics [1, 4], the combination of graph cut and geodesics [20, 9] and random walks [8].”
Fast, Exact and Multi-scale Inference for Semantic Image Segmentation with Deep Gaussian CRFs (2016, neutral): “Our findings are consistent with those of Grady in [22].”
ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation (2016, methodology): “Formulation in this form is widely used for interactive image segmentation [4, 26, 17, 11, 16, 19].”
Guide Me: Interacting with Deep Networks (2018, background): “Prior to deep learning, several systems have been proposed for semi-automatic segmentation, that allowed the user to interfere with the result or to provide hints to the system via seed points [8, 52], bounding boxes [24, 37, 54], contours or scribbles [7, 23, 58], eye gaze [26] or in the form of bi”
Geodesic Matting: A Framework for Fast Interactive Image and Video Segmentation and Matting (2009, methodology): “In [15], the author uses random walks for soft image segmentation.”
On Regularized Losses for Weakly-supervised CNN Segmentation (2018, background): “In fact, many oﬀ-line shallow interactive segmentation techniques can be used to propagate labels and generate masks, e.g. graph cuts [6,7], random walker [28,21], etc.”
FocusCut: Diving into a Focus View in Interactive Segmentation (2022, background): “develop the random walker [14] algorithm to determine the probability for each unlabeled pixel.”
DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation (2017, methodology): “Random Walks [7] that assigns a pixel with a label based on the probability that a random walker reaches a foreground or background seed first, and 4).”
Optimized Color Sampling for Robust Matting (2007, methodology): “A similar method based on Random Walks is proposed in [5].”
Interactive Object Segmentation With Inside-Outside Guidance (2020, neutral): “3 Random walker [23] 16.”
Regional Interactive Image Segmentation Networks (2017, positive): “(e) MS COCO unseen categories RIS-Net (ours) iFCN [29] Random walker [11] Euclidean star convexity [12] Geodesic star convexity [12] Geodesic matting [1] Growcut [26] Graph cut [4]”
Learning to segment under various forms of weak supervision (2015, methodology): “Supervision in the form of partial labels has been effectively utilized in interactive object segmentation via graph-cuts [3], random walks [12], geodesic shortest path [2], and geodesic star convexity [15].”
Visualizing Data using t-SNE (2008, methodology): “Alternatively, Grady (2006) presents an analytical solution to compute the pairwise similarities p j i that involves solving a sparse linear system.”