Clustering-Based, Fully Automated Mixed-Bag Jigsaw Puzzle Solving

The jig swap puzzle is a variant of the traditional jigsaw puzzle, wherein all pieces are equal-sized squares that must be placed adjacent to one another to reconstruct an original, unknown image. This paper proposes an agglomerative hierarchical clustering-based solver that can simultaneously reconstruct multiple, mixed jig swap puzzles. Our solver requires no additional information beyond an unordered input bag of puzzle pieces, and it significantly outperforms the current state of the art in terms of both the reconstructed output quality as well the number of input puzzles it supports. In addition, we define the first quality metrics specifically tailored for multi-puzzle solvers, the Enhanced Direct Accuracy Score (EDAS), the Shiftable Enhanced Direct Accuracy Score (SEDAS), and the Enhanced Neighbor Accuracy Score (ENAS).