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.
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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. https://doi.org/10.1007/978-3-319-44781-0_21
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DOI: https://doi.org/10.1007/978-3-319-44781-0_21
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