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

  • Dror Sholomon
  • Omid E. David
  • Nathan S. Netanyahu
Conference paper

DOI: 10.1007/978-3-319-44781-0_21

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9887)
Cite this paper as:
Sholomon D., David O.E., Netanyahu N.S. (2016) DNN-Buddies: A Deep Neural Network-Based Estimation Metric for the Jigsaw Puzzle Problem. In: Villa A., Masulli P., Pons Rivero A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science, vol 9887. Springer, Cham

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.

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Dror Sholomon
    • 1
  • Omid E. David
    • 1
  • Nathan S. Netanyahu
    • 1
    • 2
  1. 1.Department of Computer ScienceBar-Ilan UniversityRamat-GanIsrael
  2. 2.Center for Automation ResearchUniversity of MarylandCollege ParkUSA

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