Abstract
We present a novel, purely affinity-based natural image matting algorithm. Our method relies on carefully defined pixel-to-pixel connections that enable effective use of information available in the image. We control the information flow from the known-opacity regions into the unknown region, as well as within the unknown region itself, by utilizing multiple definitions of pixel affinities. Among other forms of information flow, we introduce color-mixture flow, which builds upon local linear embedding and effectively encapsulates the relation between different pixel opacities. Our resulting novel linear system formulation can be solved in closed-form and is robust against several fundamental challenges of natural matting such as holes and remote intricate structures. While our method is primarily designed as a standalone matting tool, we show that it can also be used for regularizing mattes obtained by sampling-based methods. The formulation is also extended to layer color estimation and we show that the use of multiple channels of flow increases the layer color quality. We also demonstrate our performance in green-screen keying and analyze the characteristics of the utilized affinities.
Abstract (translated)
我们提出了一种新的，纯粹基于亲和力的自然图像铺垫算法。我们的方法依赖于精心定义的像素到像素的连接，从而能够有效地使用图像中的可用信息。我们利用像素关联的多重定义，控制从已知不透明度区域到未知区域以及未知区域本身的信息流。在其他形式的信息流中，我们引入了基于局部线性嵌入的颜色混合流，有效地封装了不同像素不透明度之间的关系。我们的新线性系统公式可以封闭式解决，并且能够有效应对自然铺垫的几个基本挑战，如孔和远程复杂结构。虽然我们的方法主要是作为一个独立的消光工具来设计的，但是我们证明它也可以用于对基于采样的方法得到的消光片进行正则化。将该公式推广到层颜色估计中，证明了多通道流的使用提高了层颜色质量。我们还演示了我们在绿屏键控中的性能，并分析了所使用的亲缘关系的特性。
URL
https://arxiv.org/abs/1707.05055
