Multi-Phase Relaxation Labeling for Square Jigsaw Puzzle Solving

Ben Vardi, Alessandro Torcinovich, Marina Khoroshiltseva, Marcello Pelillo, Ohad Ben-Shahar

Research output: Contribution to journalConference articlepeer-review


We present a novel method for solving square jigsaw puzzles based on global optimization. The method is fully automatic, assumes no prior information, and can handle puzzles with known or unknown piece orientation. At the core of the optimization process is nonlinear relaxation labeling, a well-founded approach for deducing global solutions from local constraints, but unlike the classical scheme here we propose a multi-phase approach that guarantees convergence to feasible puzzle solutions. Next to the algorithmic novelty, we also present a new compatibility function for the quantification of the affinity between adjacent puzzle pieces. Competitive results and the advantage of the multi-phase approach are demonstrated on standard datasets.


  • Puzzle Solving
  • Relaxation Labeling
  • Square Jigsaw Puzzles

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction


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