Solving Jigsaw puzzle of large eroded gaps using puzzlet discriminant network

Solving Jigsaw puzzles has recently become an emerging research topic. Traditionally, boundary similarities are utilized for puzzle reassembly. In this paper, we solve Jigsaw Puzzles of Large Eroded Gaps (JPLEG), where boundary similarities are weak and image semantics are the only feasible clues. Inspired by human strategy in solving a puzzle, we introduce the concept of puzzlet, where fragments are gradually combined to form puzzlets of different sizes until the completion of the puzzle. Two sets of Puzzlet Discriminant Networks are designed to visually perceive whether these puzzlets are correctly reassembled. The puzzle reassembly is then formulated as a combinatorial optimization problem, and solved using a genetic algorithm. The proposed method is evaluated on two large datasets, which shows that it significantly outperforms the state-of-the-art methods for puzzle solving.