We propose an optimization framework for interactive image segmentation (IIS) that operates in bilateral space to achieve robust object extraction and instant visual feedback. More specifically, we first resample an input image using a regular bilateral grid with a resolution that is typically coarser than the input image to reduce the complexity of subsequent IIS tasks. We then design a Markov random field energy on the vertices of the bilateral grid that can be solved efficiently using a standard graph cut label assignment. To achieve this, we introduce reliable color models to distinguish the foreground and background despite the presence of extremely difficult cases and a higher-order potential to encourage spatial consistency in segmentation. We conduct comprehensive experiments on three standard interactive segmentation datasets, MSRA 10K, IIS, and PASCAL VOC 2012 segmentation validation set. The results show that the proposed method achieves competitive performance compared with state-of-the-art methods while making the current system efficient in terms of speed.

中文翻译：

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双边空间中快速而强大的交互式图像分割，具有可靠的颜色建模和更高阶的潜力

我们提出了一种交互式图像分割 (IIS) 优化框架，该框架在双边空间中运行，以实现稳健的对象提取和即时视觉反馈。更具体地说，我们首先使用分辨率通常比输入图像更粗糙的规则双边网格重新采样输入图像，以降低后续 IIS 任务的复杂性。然后，我们在双边网格的顶点上设计了一个马尔可夫随机场能量，可以使用标准的图切割标签分配来有效地解决该问题。为了实现这一点，我们引入了可靠的颜色模型来区分前景和背景，尽管存在极其困难的情况和高阶潜力，以鼓励分割中的空间一致性。我们对三个标准交互式分割数据集 MSRA 10K、IIS 和 PASCAL VOC 2012 分段验证集。结果表明，与最先进的方法相比，所提出的方法实现了具有竞争力的性能，同时使当前系统在速度方面更加高效。