# Jigsaw Puzzle Solving Using Local Feature Co-Occurrences in Deep Neural Networks

arXiv: https://arxiv.org/abs/1807.03155 · HTML: https://mlsanity.com/paper/1807.03155/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2018.

## Built on by (most cited first, 2 of 2)

- [JigsawGAN: Auxiliary Learning for Solving Jigsaw Puzzles with Generative Adversarial Networks](https://mlsanity.com/paper/2101.07555/) (2021)
- [Grid Jigsaw Representation with CLIP: A New Perspective on Image Clustering](https://mlsanity.com/paper/2310.17869/) (2023)

## Abstract (arXiv)

Archaeologists are in dire need of automated object reconstruction methods. Fragments reassembly is close to puzzle problems, which may be solved by computer vision algorithms. As they are often beaten on most image related tasks by deep learning algorithms, we study a classification method that can solve jigsaw puzzles. In this paper, we focus on classifying the relative position: given a couple of fragments, we compute their local relation (e.g. on top). We propose several enhancements over the state of the art in this domain, which is outperformed by our method by 25%. We propose an original dataset composed of pictures from the Metropolitan Museum of Art. We propose a greedy reconstruction method based on the predicted relative positions.