Jigsaw puzzle image retrieval via pairwise compatibility measurement

Abstract

To solve jigsaw puzzles, pairwise compatibility scores for all pairs should be computed first. As puzzles are reassembled according to the compatibility scores, how to measure pairwise compatibility is crucial. In this paper, we propose a novel pairwise piece compatibility scoring approach that considers not only edge similarity but also content similarity between puzzle pieces. Specifically, we propose an algorithm that computes content similarity scores between two puzzle pieces and introduce two pairwise scoring measurements that assemble the content similarity scores and edge similarity scores. We designed a jigsaw puzzle image retrieval system that identifies the best matching puzzle piece from the candidate set given a target piece to evaluate the proposed pairwise compatibility measurements. We tested our approach on the jigsaw puzzle test images, and the experimental results show our approach is comparable to the state-of-the-art and even outperforms them for some test images.