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Click-cut: a framework for interactive object selection

机译：Click-Cut：交互式对象选择的框架

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摘要

In order to simplify interactive image segmentation, We propose a new interactive segmentation framework for cutting an object from its background by which user interaction is reduced to only one click. Our proposed interactive segmentation framework consists of two steps: the image is first segmented automatically and then the object is extracted via user interaction, achieving image interactive segmentation. Combining the color and texture features of an image, We propose automatic partitioning for the image on the basis of modularity optimization. We construct the image region similarity network and partition the network into communities. We propose several region selection strategies. The user only needs to provide an interaction click. The region where the user click is merged with its adjacent regions recursively in accordance with the region selection strategy, resulting in user-desired regions. The image is finally divided into the foreground and the background. Compared with existing interactive segmentation approaches, the proposed method uses the simplest user interaction: it does not simultaneously require foreground or background markers for input. We evaluate our framework on different public image datasets. The experimental results indicate that the proposed method is superior to all existing interactive segmentation approaches. Results show that our framework achieves 67.5% accuracy on Grabcut, 80.8% accuracy on BSD_SSDS and 78.6% accuracy on MSRC_HighQuality.
机译：为了简化交互式图像分割，我们提出了一种新的交互式分段框架，用于将对象从其背景中切割，用户交互只减少为单击。我们所提出的交互式分段框架由两个步骤组成：首先自动分割图像，然后通过用户交互提取对象，实现图像交互分段。结合图像的颜色和纹理特征，我们在模块化优化的基础上提出了图像的自动分区。我们构建图像区域相似度网络并将网络分区为社区。我们提出了几个地区选择策略。用户只需要提供互动点击。根据区域选择策略，用户单击的区域与其相邻区域合并，导致用户期望的区域。图像最终分为前景和背景。与现有的交互式分割方法相比，所提出的方法使用最简单的用户交互：它不同时需要输入的前景或背景标记。我们在不同的公共图像数据集上评估我们的框架。实验结果表明，该方法优于所有现有的交互式分段方法。结果表明，我们的框架在Grabcut上实现了67.5％，对BSD_SSDS的准确性为80.8％，对MSRC_HIGHQUALIGAL的准确性为78.6％。

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