A Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles
This work addresses the challenge of automated jigsaw puzzle solving for researchers and practitioners, offering incremental improvements in speed and accuracy.
The paper tackles the problem of solving very large jigsaw puzzles by proposing a genetic algorithm-based solver that merges parent solutions to create improved child solutions, achieving state-of-the-art performance with faster and more accurate results, including solving puzzles of unprecedented size.
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.