Abstract
Image matting is generally modeled as a space transform from the color space to the alpha space. By estimating the alpha factor of the model, the foreground of an image can be extracted. However, there is some dimensional information redundancy in the alpha space. It usually leads to the misjudgments of some pixels near the boundary between the foreground and the background. In this paper, a manifold matting framework named Patch Alignment Manifold Matting is proposed for image matting. In particular, we first propose a part modeling of color space in the local image patch. We then perform whole alignment optimization for approximating the alpha results using subspace reconstructing error. Furthermore, we utilize Nesterov's algorithm to solve the optimization problem. Finally, we apply some manifold learning methods in the framework, and obtain several image matting methods, such as named ISOMAP matting and its derived Cascade ISOMAP matting. The experimental results reveal that the manifold matting framework and its two examples are effective when compared with several representative matting methods.
Abstract (translated)
图像铺垫通常被建模为从颜色空间到alpha空间的空间转换。通过估计模型的α因子，可以提取图像的前景。然而，在alpha空间中存在一些维度信息冗余。它通常会导致在前景和背景之间的边界附近对一些像素的错误判断。本文提出了一种用于图像铺垫的流形铺垫框架，称为贴片对齐流形铺垫。特别地，我们首先提出了局部图像补丁中颜色空间的部分建模。然后利用子空间重构误差对α结果进行全对准优化。此外，我们还利用Nesterov算法来解决优化问题。最后，在该框架中应用了多种学习方法，得到了几种图像匹配方法，如等差映射映射映射及其派生的级联等差映射映射映射映射映射映射。实验结果表明，与几种典型的铺垫方法相比，流形铺垫框架及其两个实例是有效的。
URL
https://arxiv.org/abs/1904.07588
