2013
CVPR
CVPR 2013
A Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles
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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Conference Pioneer
- CVPR 2013
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Topic Pioneer
- Evolutionary Algorithm
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Interdisciplinary Bridge
- Computer Science and Machine Learning and Mathematics & Optimization
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Trend Setter
- Computer Vision
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Keyword Pioneer
- pattern matching
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Hot Topic Early Bird
- combinatorial optimization
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Cross-Pollinator
- Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio