Efficient affinity-based edit propagation using K-D tree

  • Authors:

  • Kun Xu;Yong Li;Tao Ju;Shi-Min Hu;Tian-Qiang Liu

  • Affiliations:

  • Tsinghua University;Tsinghua University;Washington University in St. Louis;Tsinghua University;Tsinghua University

  • Venue:
  • ACM SIGGRAPH Asia 2009 papers
  • Year:
  • 2009

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Abstract

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.