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.

翻译：尽管取得了巨大进展，但在复杂背景、强度变化剧烈及拓扑形态多样的场景中，实现精确图像分割仍然是一项具有挑战性的任务。最小路径模型在解决图像分割问题中展现出强大能力，然而此类方法的分割性能严重受制于模型初始化过程，从而限制了其实际应用范围。本文提出一种新型掩膜提议投票框架，克服了经典方法的主要缺陷，即使在复杂场景下也能实现鲁棒分割。首先，我们引入一种高效的自适应域割构建方法，将其作为约束条件初始化基于区域的极小割演化过程，从而生成多样且可靠的掩膜提议候选集，显著提升这些候选区域精确覆盖目标区域的概率。其次，我们提出一种新型掩膜投票机制，构建编码最终分割信息的投票得分图。与经典路径投票方法不同，本模型允许通过引入先验信息为每个独立掩膜分配不同权重。因此，所提出的分割模型能够在复杂场景下精确描绘目标边界，且对初始化不敏感。实验表明，本方法在精确度和鲁棒性上始终优于现有最优的最小路径分割方法。