DNN-Buddies: A Deep Neural Network-Based Estimation Metric for the Jigsaw Puzzle Problem
Computer Vision and Pattern Recognition
2017-11-27 v1 Machine Learning
Neural and Evolutionary Computing
Machine Learning
Abstract
This paper introduces the first deep neural network-based estimation metric for the jigsaw puzzle problem. Given two puzzle piece edges, the neural network predicts whether or not they should be adjacent in the correct assembly of the puzzle, using nothing but the pixels of each piece. The proposed metric exhibits an extremely high precision even though no manual feature extraction is performed. When incorporated into an existing puzzle solver, the solution's accuracy increases significantly, achieving thereby a new state-of-the-art standard.
Cite
@article{arxiv.1711.08762,
title = {DNN-Buddies: A Deep Neural Network-Based Estimation Metric for the Jigsaw Puzzle Problem},
author = {Dror Sholomon and Eli David and Nathan S. Netanyahu},
journal= {arXiv preprint arXiv:1711.08762},
year = {2017}
}