Rotation symmetry object classification using structure constrained convolutional neural network

Seunghwa Yu; Seugnkyu Lee

doi:10.1007/978-3-030-03801-4_13

Rotation symmetry object classification using structure constrained convolutional neural network

Seunghwa Yu, Seugnkyu Lee

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

1 Citation (Scopus)

Abstract

Rotation symmetry is a salient visual clue in describing and recognizing an object or a structure in an image. Recently, various rotation symmetry detection methods have been proposed based on key point feature matching scheme. However, hand crafted representation of rotation symmetry structure has shown limited performance. On the other hand, deep learning based approach has been rarely applied to symmetry detection due to the huge diversity in the visual appearance of rotation symmetry patterns. In this work, we propose a new framework of convolutional neural network based on two core layers: rotation invariant convolution (RI-CONV) layer and symmetry structure constrained convolution (SSC-CONV) layer. Proposed network learns structural characteristic from image samples regardless of their appearance diversity. Evaluation is conducted on 32,000 images (after augmentation) of our rotation symmetry classification data set.

Original language	English
Title of host publication	Advances in Visual Computing - 13th International Symposium, ISVC 2018, Proceedings
Editors	Kai Xu, Stephen Lin, Richard Boyle, Bilal Alsallakh, Matt Turek, Srikumar Ramalingam, George Bebis, Bahram Parvin, Jing Yang, Jonathan Ventura, Darko Koracin, Eduardo Cuervo
Publisher	Springer Verlag
Pages	139-146
Number of pages	8
ISBN (Print)	9783030038007
DOIs	https://doi.org/10.1007/978-3-030-03801-4_13
Publication status	Published - 2018
Event	13th International Symposium on Visual Computing, ISVC 2018 - Las Vegas, NV, United States Duration: 19 Nov 2018 → 21 Nov 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11241 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Symposium on Visual Computing, ISVC 2018
Country/Territory	United States
City	Las Vegas, NV
Period	19/11/18 → 21/11/18

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2018.

Keywords

CNN
Rotation symmetry classification

Access to Document

10.1007/978-3-030-03801-4_13

Cite this

Yu, S., & Lee, S. (2018). Rotation symmetry object classification using structure constrained convolutional neural network. In K. Xu, S. Lin, R. Boyle, B. Alsallakh, M. Turek, S. Ramalingam, G. Bebis, B. Parvin, J. Yang, J. Ventura, D. Koracin, & E. Cuervo (Eds.), Advances in Visual Computing - 13th International Symposium, ISVC 2018, Proceedings (pp. 139-146). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11241 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-03801-4_13

Yu, Seunghwa ; Lee, Seugnkyu. / Rotation symmetry object classification using structure constrained convolutional neural network. Advances in Visual Computing - 13th International Symposium, ISVC 2018, Proceedings. editor / Kai Xu ; Stephen Lin ; Richard Boyle ; Bilal Alsallakh ; Matt Turek ; Srikumar Ramalingam ; George Bebis ; Bahram Parvin ; Jing Yang ; Jonathan Ventura ; Darko Koracin ; Eduardo Cuervo. Springer Verlag, 2018. pp. 139-146 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Rotation symmetry object classification using structure constrained convolutional neural network",

abstract = "Rotation symmetry is a salient visual clue in describing and recognizing an object or a structure in an image. Recently, various rotation symmetry detection methods have been proposed based on key point feature matching scheme. However, hand crafted representation of rotation symmetry structure has shown limited performance. On the other hand, deep learning based approach has been rarely applied to symmetry detection due to the huge diversity in the visual appearance of rotation symmetry patterns. In this work, we propose a new framework of convolutional neural network based on two core layers: rotation invariant convolution (RI-CONV) layer and symmetry structure constrained convolution (SSC-CONV) layer. Proposed network learns structural characteristic from image samples regardless of their appearance diversity. Evaluation is conducted on 32,000 images (after augmentation) of our rotation symmetry classification data set.",

keywords = "CNN, Rotation symmetry classification",

author = "Seunghwa Yu and Seugnkyu Lee",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 13th International Symposium on Visual Computing, ISVC 2018 ; Conference date: 19-11-2018 Through 21-11-2018",

year = "2018",

doi = "10.1007/978-3-030-03801-4_13",

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

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Yu, S & Lee, S 2018, Rotation symmetry object classification using structure constrained convolutional neural network. in K Xu, S Lin, R Boyle, B Alsallakh, M Turek, S Ramalingam, G Bebis, B Parvin, J Yang, J Ventura, D Koracin & E Cuervo (eds), Advances in Visual Computing - 13th International Symposium, ISVC 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11241 LNCS, Springer Verlag, pp. 139-146, 13th International Symposium on Visual Computing, ISVC 2018, Las Vegas, NV, United States, 19/11/18. https://doi.org/10.1007/978-3-030-03801-4_13

Rotation symmetry object classification using structure constrained convolutional neural network. / Yu, Seunghwa; Lee, Seugnkyu.
Advances in Visual Computing - 13th International Symposium, ISVC 2018, Proceedings. ed. / Kai Xu; Stephen Lin; Richard Boyle; Bilal Alsallakh; Matt Turek; Srikumar Ramalingam; George Bebis; Bahram Parvin; Jing Yang; Jonathan Ventura; Darko Koracin; Eduardo Cuervo. Springer Verlag, 2018. p. 139-146 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11241 LNCS).

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

TY - GEN

T1 - Rotation symmetry object classification using structure constrained convolutional neural network

AU - Yu, Seunghwa

AU - Lee, Seugnkyu

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2018.

PY - 2018

Y1 - 2018

N2 - Rotation symmetry is a salient visual clue in describing and recognizing an object or a structure in an image. Recently, various rotation symmetry detection methods have been proposed based on key point feature matching scheme. However, hand crafted representation of rotation symmetry structure has shown limited performance. On the other hand, deep learning based approach has been rarely applied to symmetry detection due to the huge diversity in the visual appearance of rotation symmetry patterns. In this work, we propose a new framework of convolutional neural network based on two core layers: rotation invariant convolution (RI-CONV) layer and symmetry structure constrained convolution (SSC-CONV) layer. Proposed network learns structural characteristic from image samples regardless of their appearance diversity. Evaluation is conducted on 32,000 images (after augmentation) of our rotation symmetry classification data set.

AB - Rotation symmetry is a salient visual clue in describing and recognizing an object or a structure in an image. Recently, various rotation symmetry detection methods have been proposed based on key point feature matching scheme. However, hand crafted representation of rotation symmetry structure has shown limited performance. On the other hand, deep learning based approach has been rarely applied to symmetry detection due to the huge diversity in the visual appearance of rotation symmetry patterns. In this work, we propose a new framework of convolutional neural network based on two core layers: rotation invariant convolution (RI-CONV) layer and symmetry structure constrained convolution (SSC-CONV) layer. Proposed network learns structural characteristic from image samples regardless of their appearance diversity. Evaluation is conducted on 32,000 images (after augmentation) of our rotation symmetry classification data set.

KW - CNN

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SN - 9783030038007

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

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EP - 146

BT - Advances in Visual Computing - 13th International Symposium, ISVC 2018, Proceedings

A2 - Xu, Kai

A2 - Lin, Stephen

A2 - Boyle, Richard

A2 - Alsallakh, Bilal

A2 - Turek, Matt

A2 - Ramalingam, Srikumar

A2 - Bebis, George

A2 - Parvin, Bahram

A2 - Yang, Jing

A2 - Ventura, Jonathan

A2 - Koracin, Darko

A2 - Cuervo, Eduardo

PB - Springer Verlag

T2 - 13th International Symposium on Visual Computing, ISVC 2018

Y2 - 19 November 2018 through 21 November 2018

ER -

Yu S, Lee S. Rotation symmetry object classification using structure constrained convolutional neural network. In Xu K, Lin S, Boyle R, Alsallakh B, Turek M, Ramalingam S, Bebis G, Parvin B, Yang J, Ventura J, Koracin D, Cuervo E, editors, Advances in Visual Computing - 13th International Symposium, ISVC 2018, Proceedings. Springer Verlag. 2018. p. 139-146. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-03801-4_13

Rotation symmetry object classification using structure constrained convolutional neural network

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