Dnn-buddies: A deep neural network-based estimation metric for the jigsaw puzzle problem

Dror Sholomon; Omid E. David; Nathan S. Netanyahu

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

Dnn-buddies: A deep neural network-based estimation metric for the jigsaw puzzle problem

Dror Sholomon, Omid E. David, Nathan S. Netanyahu

University of Maryland, College Park

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Scopus citations

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.

Original language	English
Title of host publication	Artificial Neural Networks and Machine Learning - 25th International Conference on Artificial Neural Networks, ICANN 2016, Proceedings
Editors	Alessandro E.P. Villa, Paolo Masulli, Antonio Javier Pons Rivero
Publisher	Springer Verlag
Pages	170-178
Number of pages	9
ISBN (Print)	9783319447803
DOIs	https://doi.org/10.1007/978-3-319-44781-0_21
State	Published - 2016
Event	25th International Conference on Artificial Neural Networks and Machine Learning, ICANN 2016 - Barcelona, Spain Duration: 6 Sep 2016 → 9 Sep 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9887 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	25th International Conference on Artificial Neural Networks and Machine Learning, ICANN 2016
Country/Territory	Spain
City	Barcelona
Period	6/09/16 → 9/09/16

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2016.

Access to Document

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

Cite this

Sholomon, D., David, O. E., & Netanyahu, N. S. (2016). Dnn-buddies: A deep neural network-based estimation metric for the jigsaw puzzle problem. In A. E. P. Villa, P. Masulli, & A. J. P. Rivero (Eds.), Artificial Neural Networks and Machine Learning - 25th International Conference on Artificial Neural Networks, ICANN 2016, Proceedings (pp. 170-178). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9887 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-44781-0_21

Sholomon, Dror ; David, Omid E. ; Netanyahu, Nathan S. / Dnn-buddies : A deep neural network-based estimation metric for the jigsaw puzzle problem. Artificial Neural Networks and Machine Learning - 25th International Conference on Artificial Neural Networks, ICANN 2016, Proceedings. editor / Alessandro E.P. Villa ; Paolo Masulli ; Antonio Javier Pons Rivero. Springer Verlag, 2016. pp. 170-178 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{3fdbc6db3d234d59a5bd433da85c49ad,

title = "Dnn-buddies: A deep neural network-based estimation metric for the jigsaw puzzle problem",

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{\textquoteright}s accuracy increases significantly, achieving thereby a new state-of-the-art standard.",

author = "Dror Sholomon and David, {Omid E.} and Netanyahu, {Nathan S.}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 25th International Conference on Artificial Neural Networks and Machine Learning, ICANN 2016 ; Conference date: 06-09-2016 Through 09-09-2016",

year = "2016",

doi = "10.1007/978-3-319-44781-0_21",

language = "אנגלית",

isbn = "9783319447803",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "170--178",

editor = "Villa, {Alessandro E.P.} and Paolo Masulli and Rivero, {Antonio Javier Pons}",

booktitle = "Artificial Neural Networks and Machine Learning - 25th International Conference on Artificial Neural Networks, ICANN 2016, Proceedings",

address = "גרמניה",

}

Sholomon, D, David, OE & Netanyahu, NS 2016, Dnn-buddies: A deep neural network-based estimation metric for the jigsaw puzzle problem. in AEP Villa, P Masulli & AJP Rivero (eds), Artificial Neural Networks and Machine Learning - 25th International Conference on Artificial Neural Networks, ICANN 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9887 LNCS, Springer Verlag, pp. 170-178, 25th International Conference on Artificial Neural Networks and Machine Learning, ICANN 2016, Barcelona, Spain, 6/09/16. https://doi.org/10.1007/978-3-319-44781-0_21

Dnn-buddies: A deep neural network-based estimation metric for the jigsaw puzzle problem. / Sholomon, Dror; David, Omid E.; Netanyahu, Nathan S.
Artificial Neural Networks and Machine Learning - 25th International Conference on Artificial Neural Networks, ICANN 2016, Proceedings. ed. / Alessandro E.P. Villa; Paolo Masulli; Antonio Javier Pons Rivero. Springer Verlag, 2016. p. 170-178 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9887 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Dnn-buddies

T2 - 25th International Conference on Artificial Neural Networks and Machine Learning, ICANN 2016

AU - Sholomon, Dror

AU - David, Omid E.

AU - Netanyahu, Nathan S.

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84988418468&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-44781-0_21

DO - 10.1007/978-3-319-44781-0_21

M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???

AN - SCOPUS:84988418468

SN - 9783319447803

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 170

EP - 178

BT - Artificial Neural Networks and Machine Learning - 25th International Conference on Artificial Neural Networks, ICANN 2016, Proceedings

A2 - Villa, Alessandro E.P.

A2 - Masulli, Paolo

A2 - Rivero, Antonio Javier Pons

PB - Springer Verlag

Y2 - 6 September 2016 through 9 September 2016

ER -

Sholomon D, David OE, Netanyahu NS. Dnn-buddies: A deep neural network-based estimation metric for the jigsaw puzzle problem. In Villa AEP, Masulli P, Rivero AJP, editors, Artificial Neural Networks and Machine Learning - 25th International Conference on Artificial Neural Networks, ICANN 2016, Proceedings. Springer Verlag. 2016. p. 170-178. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-44781-0_21

Dnn-buddies: A deep neural network-based estimation metric for the jigsaw puzzle problem

Abstract

Publication series

Conference

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this