A Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles

Dror Sholomon, Omid David, Nathan S. Netanyahu

CVPR 2013

doi:10.1109/CVPR.2013.231 /cvpr/2013/sholomon2013cvpr-genetic/

Abstract

In this paper we propose the first effective automated, genetic algorithm (GA)-based jigsaw puzzle solver. We introduce a novel procedure of merging two "parent" solutions to an improved "child" solution by detecting, extracting, and combining correctly assembled puzzle segments. The solver proposed exhibits state-of-the-art performance solving previously attempted puzzles faster and far more accurately, and also puzzles of size never before attempted. Other contributions include the creation of a benchmark of large images, previously unavailable. We share the data sets and all of our results for future testing and comparative evaluation of jigsaw puzzle solvers.

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Cite

Text

Sholomon et al. "A Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles." Conference on Computer Vision and Pattern Recognition, 2013. doi:10.1109/CVPR.2013.231

Markdown

[Sholomon et al. "A Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles." Conference on Computer Vision and Pattern Recognition, 2013.](https://mlanthology.org/cvpr/2013/sholomon2013cvpr-genetic/) doi:10.1109/CVPR.2013.231

BibTeX

@inproceedings{sholomon2013cvpr-genetic,
  title     = {{A Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles}},
  author    = {Sholomon, Dror and David, Omid and Netanyahu, Nathan S.},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2013},
  doi       = {10.1109/CVPR.2013.231},
  url       = {https://mlanthology.org/cvpr/2013/sholomon2013cvpr-genetic/}
}