Mask Proposal Voting Based on Geodesic Framework for Robust Image Segmentation

2026-06-12

AI Summary

This work addresses the limitations of traditional minimal path-based image segmentation methods, which suffer from insufficient accuracy in complex backgrounds and high sensitivity to initialization. To overcome these issues, the authors propose a geodesic framework–based mask proposal voting mechanism that generates diverse and reliable mask candidates through adaptive domain partitioning and constructs the final segmentation via a voting strategy incorporating prior knowledge. The approach innovatively integrates region-based min-cut evolution, geodesic distance computation, and mask scoring maps, effectively eliminating dependence on initial seed points. Experimental results demonstrate that the proposed method significantly outperforms existing minimal path segmentation techniques across multiple challenging scenarios, achieving state-of-the-art performance in both robustness and accuracy.

Abstract

Despite great advances, finding accurate segmentation remains a challenging task, especially in scenarios with cluttered backgrounds, complex intensity variations and topology appearance. Minimal path models have exhibited their strong ability in addressing image segmentation tasks. However, the performance of minimal paths-based segmentation approaches is heavily influenced by model initialization, hence limiting their application scope in practice. In this work, we propose a novel mask proposal voting framework that overcomes the major drawback of classical approaches, allowing robust segmentation even in complicated scenarios. Firstly, we introduce an efficient method for constructing adaptive domain cuts as a constraint for initializing the region-based min-cut evolution, by which diverse and reliable mask proposal candidates can be generated, substantially increasing the possibility of accurately covering the objective region by these proposals. Secondly, we propose a new mask voting scheme to build a voting score map encoding the final segmentation information. In contrast to classical path voting methods, our model allows incorporating priors to assign different importance to each individual mask. As a consequence, the proposed segmentation model is capable of accurately delineating object boundaries under complex scenarios, and is insensitive to initialization. Experiments demonstrate that our method consistently outperforms state-of-the-art minimal path-based approaches in both accuracy and robustness.

Problem

• image segmentation
• minimal path
• initialization sensitivity
• robustness
• complex scenarios

Innovation

• mask proposal voting
• geodesic framework
• adaptive domain cuts
• min-cut evolution
• robust segmentation