A Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles

Dror Sholomon, Omid David, Nathan S. Netanyahu; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 1767-1774

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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[bibtex]
@InProceedings{Sholomon_2013_CVPR,
author = {Sholomon, Dror and David, Omid and Netanyahu, Nathan S.},
title = {A Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2013}
}