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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References
Altman, T.: Solving the jigsaw puzzle problem in linear time. Appl. Artif. Intell. Int. J. 3(4), 453–462 (1989)
Brown, B., Toler-Franklin, C., Nehab, D., Burns, M., Dobkin, D., Vlachopoulos, A., Doumas, C., Rusinkiewicz, S., Weyrich, T.: A system for high-volume acquisition and matching of fresco fragments: Reassembling Theran wall paintings. ACM Trans. Graph. 27(3), 84 (2008)
Cao, S., Liu, H., Yan, S.: Automated assembly of shredded pieces from multiple photos. In: IEEE International Conference on Multimedia and Expo, pp. 358–363 (2010)
Cho, T., Avidan, S., Freeman, W.: A probabilistic image jigsaw puzzle solver. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 183–190 (2010)
Cho, T., Butman, M., Avidan, S., Freeman, W.: The patch transform and its applications to image editing. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Collobert, R., Kavukcuoglu, K., Farabet, C.: Torch7: a matlab-like environment for machine learning. In: BigLearn, NIPS Workshop. No. EPFL-CONF-192376 (2011)
Deever, A., Gallagher, A.: Semi-automatic assembly of real cross-cut shredded documents. In: ICIP, pp. 233–236 (2012)
Demaine, E., Demaine, M.: Jigsaw puzzles, edge matching, and polyomino packing: Connections and complexity. Graphs Comb. 23, 195–208 (2007)
Freeman, H., Garder, L.: Apictorial jigsaw puzzles: The computer solution of a problem in pattern recognition. IEEE Trans. Electron. Comput. EC-13(2), 118–127 (1964)
Gallagher, A.: Jigsaw puzzles with pieces of unknown orientation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 382–389 (2012)
Goldberg, D., Malon, C., Bern, M.: A global approach to automatic solution of jigsaw puzzles. Comput. Geom.: Theory Appl. 28(2–3), 165–174 (2004)
Grubinger, M., Clough, P., Müller, H., Deselaers, T.: The IAPR TC-12 benchmark: a new evaluation resource for visual information systems. In: International Workshop OntoImage, vol. 5, p. 10 (2006)
He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263–1284 (2009)
Justino, E., Oliveira, L., Freitas, C.: Reconstructing shredded documents through feature matching. Forensic Sci. Int. 160(2), 140–147 (2006)
Koller, D., Levoy, M.: Computer-aided reconstruction and new matches in the forma urbis romae. Bullettino Della Commissione Archeologica Comunale di Roma, 103–125 (2006)
Marande, W., Burger, G.: Mitochondrial DNA as a genomic jigsaw puzzle. Science 318(5849), 415–415 (2007)
Marques, M., Freitas, C.: Reconstructing strip-shredded documents using color as feature matching. In: ACM Symposium on Applied Computing, pp. 893–894 (2009)
Morton, A.Q., Levison, M.: The computer in literary studies. In: IFIP Congress, pp. 1072–1081 (1968)
Paikin, G., Tal, A.: Solving multiple square jigsaw puzzles with missing pieces. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition, pp. 4832–4839. IEEE (2015)
Pomeranz, D., Shemesh, M., Ben-Shahar, O.: A fully automated greedy square jigsaw puzzle solver. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 9–16 (2011)
Sholomon, D., David, O.E., Netanyahu, N.S.: A genetic algorithm-based solver for very large jigsaw puzzles. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1767–1774 (2013)
Sholomon, D., David, O.E., Netanyahu, N.S.: A generalized genetic algorithm-based solver for very large jigsaw puzzles of complex types. In: AAAI Conference on Artificial Intelligence, pp. 2839–2845 (2014)
Sholomon, D., David, O.E., Netanyahu, N.S.: Genetic algorithm-based solver for very large multiple jigsaw puzzles of unknown dimensions and piece orientation. In: ACM Conference on Genetic and Evolutionary Computation, pp. 1191–1198 (2014)
Son, K., Hays, J., Cooper, D.B.: Solving square jigsaw puzzles with loop constraints. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part VI. LNCS, vol. 8694, pp. 32–46. Springer, Heidelberg (2014)
Wang, C.S.E.: Determining molecular conformation from distance or density data. Ph.D. Thesis, Massachusetts Institute of Technology (2000)
Yang, X., Adluru, N., Latecki, L.J.: Particle filter with state permutations for solving image jigsaw puzzles. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2873–2880 (2011)
Zhao, Y., Su, M., Chou, Z., Lee, J.: A puzzle solver and its application in speech descrambling. In: WSEAS International Conference on Computer Engineering and Applications, pp. 171–176 (2007)
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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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