When reconstructing jigsaw puzzles, the state-of-the-art algorithms struggle to distinguish between identically colored pieces that belong to different objects. This limitation significantly impacts the accuracy of puzzle solvers, especially in complex images with repetitive colors or textures. To address this issue, we propose a new GA-based square jigsaw puzzle solver. A combined color and texture discriminator is incorporated into the proposed solver to prevent pieces that have the same color but come from distinct objects from being joined together incorrectly. Color and texture features are extracted separately using the sum of square distances and Gabor filter. To evaluate the performance of the proposed solver, we used a dataset consisting 66 images: 20 puzzles with 432 pieces from the MIT collection, 20 puzzles with 540 pieces, and 20 puzzles with 805 pieces from the McGill collection, and 3 puzzles with 2360 pieces, and 3 puzzles with 3300 pieces from the Pomeranz collection. For the direct, neighbor, and largest component comparisons, the proposed method’s accuracy is 92.91%, 96.66%, and 90.83%, respectively. The proposed method demonstrates an improvement of 11.9%, and 3.65% in accuracy based on direct and neighbor comparison criteria, on the database images when compared to current state-of-the-art GA-based square jigsaw puzzle solver.

Jigsaw puzzle solving, Genetic algorithm, Compatibility criterion, Texture feature, Gabor filter