In the task of interactive image segmentation, the user initially indicates some pixels as target seeds and background seeds and propagates the seeds’ labels to the rest of the image. We propose an equidistant seed generation method for interactive image segmentation that combines saliency detection and random walk (RW) with restart with optimal restarting probability. Our method includes two stages: first, we utilize the saliency detection method to obtain the initial target region and generate the initial seeds. Then we relabel the seeds using a Gaussian mixture model with limited user interaction to obtain accurate seeds. The relabeled seeds can limit the range of object; therefore, in the second stage, we can select an optimal restarting probability by calculating the area of the RW that exceeds the limited range of the object seeds. Our method can generate effective seeds and thus reduce the dependence on user interaction. Furthermore, using the optimal restarting probability, we can obtain higher segmentation accuracy. Extensive experiments on the Berkeley segmentation dataset, GrabCut dataset, and MSRA1000 dataset demonstrate that our method can reduce the dependence on user interaction and achieve better performance than the latest interactive image segmentation methods, such as RW, RW with restart, normalized RW, sub-Markov RW, and label propagation through complex networks.

中文翻译：

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基于显着性的种子生成和随机游走与重新启动图像分割的集成

在交互式图像分割任务中，用户最初将一些像素指定为目标种子和背景种子，并将种子的标签传播到图像的其余部分。我们提出了一种用于交互式图像分割的等距种子生成方法，该方法将显着性检测和随机游走 (RW) 与具有最佳重启概率的重启相结合。我们的方法包括两个阶段：首先，我们利用显着性检测方法获得初始目标区域并生成初始种子。然后我们使用具有有限用户交互的高斯混合模型重新标记种子以获得准确的种子。重新标记的种子可以限制对象的范围；因此，在第二阶段，我们可以通过计算超出对象种子限制范围的 RW 区域来选择最佳重启概率。我们的方法可以生成有效的种子，从而减少对用户交互的依赖。此外，使用最优重启概率，我们可以获得更高的分割精度。在 Berkeley 分割数据集、GrabCut 数据集和 MSRA1000 数据集上的大量实验表明，我们的方法可以减少对用户交互的依赖，并取得比最新的交互式图像分割方法更好的性能，例如 RW、RW with restart、normalized RW、sub- Markov RW，以及通过复杂网络的标签传播。