A Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles

TL;DR

This paper presents a novel genetic algorithm-based method for solving very large jigsaw puzzles, achieving state-of-the-art accuracy and speed, and introduces a new benchmark dataset for large puzzles.

Contribution

It introduces a new GA-based approach with a novel solution merging procedure and provides the first benchmark dataset for large jigsaw puzzles.

Findings

Achieved faster and more accurate puzzle solving than previous methods.

Successfully solved puzzles of unprecedented size.

Provided publicly available datasets for future research.

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.