A Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles

Author

Sholomon, Dror ; David, Olivier ; Netanyahu, Nathan S.

Author_Institution

Center for Autom. Res., Univ. of Maryland, College Park, MD, USA

fYear

2013

fDate

23-28 June 2013

Firstpage

1767

Lastpage

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.

Keywords

benchmark testing; computer games; feature extraction; genetic algorithms; performance evaluation; assembled puzzle segment detection; assembled puzzle segment extraction; automated genetic algorithm based jigsaw puzzle solver; data sets; genetic algorithm-based solver; image benchmark; improved child solution; parent solutions; Benchmark testing; Biological cells; Genetic algorithms; Image segmentation; Kernel; Sociology; Statistics; Genetic Algorithms; Jigsaw Puzzle;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on

Conference_Location

Portland, OR

ISSN

1063-6919

Type

conf

DOI

10.1109/CVPR.2013.231

Filename

6619075