# Grouping Boundary Proposals for Fast Interactive Image Segmentation

arXiv: https://arxiv.org/abs/2309.04169 · HTML: https://mlsanity.com/paper/2309.04169/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 54 references that are in the map.
- First posted on arXiv 8 Sep 2023.

## Builds on (most cited first, 8 of 8)

- [DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation](https://mlsanity.com/paper/1707.00652/) (2017)
- [PolarMask++: Enhanced Polar Representation for Single-Shot Instance Segmentation and Beyond](https://mlsanity.com/paper/2105.02184/) (2021)
- [Global Minimum for a Finsler Elastica Minimal Path Approach](https://mlsanity.com/paper/1612.00343/) (2016)
- [A Region-based Randers Geodesic Approach for Image Segmentation](https://mlsanity.com/paper/1912.10122/) (2019)
- [Trajectory Grouping with Curvature Regularization for Tubular Structure Tracking](https://mlsanity.com/paper/2003.03710/) (2020)
- [Geodesic Paths for Image Segmentation with Implicit Region-based Homogeneity Enhancement](https://mlsanity.com/paper/2008.06909/) (2020)
- [Geodesic Models with Convexity Shape Prior](https://mlsanity.com/paper/2111.00794/) (2021)
- [Fast Asymmetric Fronts Propagation for Image Segmentation](https://mlsanity.com/paper/1707.02403/) (2017)

## Built on by (most cited first, 4 of 4)

- [Background Matters: A Cross-view Bidirectional Modeling Framework for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2505.16625/) (2025)
- [Topology-Aware Skeleton Detection via Lighthouse-Guided Structured Inference](https://mlsanity.com/paper/2604.20123/) (2026)
- [Geodesics with Unified Tangent-constrained Priors and Curvature Regularization](https://mlsanity.com/paper/2606.00139/) (2026)
- [Mask Proposal Voting Based on Geodesic Framework for Robust Image Segmentation](https://mlsanity.com/paper/2606.14912/) (2026)

## Abstract (arXiv)

Geodesic models are known as an efficient tool for solving various image segmentation problems. Most of existing approaches only exploit local pointwise image features to track geodesic paths for delineating the objective boundaries. However, such a segmentation strategy cannot take into account the connectivity of the image edge features, increasing the risk of shortcut problem, especially in the case of complicated scenario. In this work, we introduce a new image segmentation model based on the minimal geodesic framework in conjunction with an adaptive cut-based circular optimal path computation scheme and a graph-based boundary proposals grouping scheme. Specifically, the adaptive cut can disconnect the image domain such that the target contours are imposed to pass through this cut only once. The boundary proposals are comprised of precomputed image edge segments, providing the connectivity information for our segmentation model. These boundary proposals are then incorporated into the proposed image segmentation model, such that the target segmentation contours are made up of a set of selected boundary proposals and the corresponding geodesic paths linking them. Experimental results show that the proposed model indeed outperforms state-of-the-art minimal paths-based image segmentation approaches.