Efficient affinity-based edit propagation using K-D tree
Efficient affinity-based edit propagation using K-D tree
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DOI：
10.1145/1661412.1618464
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发表时间：
2009-12
期刊：
影响因子：
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通讯作者：
Kun Xu;Yong Li;T. Ju;Shimin Hu;Tian-Qiang Liu
中科院分区：
文献类型：
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作者：
Kun Xu;Yong Li;T. Ju;Shimin Hu;Tian-Qiang Liu
通过笔划进行的图像/视频编辑由于易于交互而变得越来越流行。然而，将用户输入复制到图像/视频的其余部分通常是耗时和存储器消耗的，特别是对于大数据。我们在这里提出了一个有效的方案，允许基于亲和力的编辑传播计算的数据包含数以千万计的像素在交互速率（在几秒钟内）。在我们的计划中的关键是一种新的手段近似解决编辑传播中涉及的优化问题，在高维，亲和空间中使用自适应聚类。我们的近似显着降低了现有的基于亲和力的传播方法的成本，同时保持视觉保真度，并使交互式基于笔划的编辑，即使在高分辨率图像和长视频序列使用商品计算机。
Image/video editing by strokes has become increasingly popular due to the ease of interaction. Propagating the user inputs to the rest of the image/video, however, is often time and memory consuming especially for large data. We propose here an efficient scheme that allows affinity-based edit propagation to be computed on data containing tens of millions of pixels at interactive rate (in matter of seconds). The key in our scheme is a novel means for approximately solving the optimization problem involved in edit propagation, using adaptive clustering in a high-dimensional, affinity space. Our approximation significantly reduces the cost of existing affinity-based propagation methods while maintaining visual fidelity, and enables interactive stroke-based editing even on high resolution images and long video sequences using commodity computers.