DNN-Buddies: A Deep Neural Network-Based Estimation Metric for the Jigsaw Puzzle Problem

TL;DR

This paper presents a novel deep neural network-based metric for jigsaw puzzle assembly that predicts adjacency of puzzle pieces using only pixel data, significantly improving solution accuracy and setting new standards.

Contribution

It introduces the first neural network-based estimation metric for jigsaw puzzles that requires no manual feature extraction and enhances puzzle solving accuracy.

Findings

Achieves high precision in predicting piece adjacency

Significantly improves puzzle solver accuracy

Sets new state-of-the-art performance

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.