A Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles
Computer Vision and Pattern Recognition
2017-11-21 v1 Neural and Evolutionary Computing
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.
Keywords
Cite
@article{arxiv.1711.06769,
title = {A Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles},
author = {Dror Sholomon and Eli David and Nathan S. Netanyahu},
journal= {arXiv preprint arXiv:1711.06769},
year = {2017}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1711.06767