Near-Optimal Joint Object Matching via Convex Relaxation

  title={Near-Optimal Joint Object Matching via Convex Relaxation},
  author={Yuxin Chen and Leonidas J. Guibas and Qi-Xing Huang},
Joint matching over a collection of objects aims at aggregating information from a large collection of similar instances (e.g. images, graphs, shapes) to improve maps between pairs of them. Given multiple objects and matches computed between a few object pairs in isolation, the goal is to recover an entire collection of maps that are (1) globally consistent, and (2) close to the provided maps — and under certain conditions provably the ground-truth maps. Despite recent advances on this problem… CONTINUE READING
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