A genetic algorithm-based solver for very large jigsaw puzzles

Dror Sholomon; Omid David; Nathan S. Netanyahu

doi:10.1109/CVPR.2013.231

A genetic algorithm-based solver for very large jigsaw puzzles

Dror Sholomon, Omid David, Nathan S. Netanyahu

University of Maryland, College Park

Research output: Contribution to journal › Conference article › peer-review

64 Scopus citations

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.

Original language	English
Article number	6619075
Pages (from-to)	1767-1774
Number of pages	8
Journal	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
DOIs	https://doi.org/10.1109/CVPR.2013.231
State	Published - 2013
Event	26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013 - Portland, OR, United States Duration: 23 Jun 2013 → 28 Jun 2013

Keywords

Genetic Algorithms
Jigsaw Puzzle

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10.1109/CVPR.2013.231

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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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A genetic algorithm-based solver for very large jigsaw puzzles

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