Abstract
In recent years, there has been renewed interest in bilateral symmetry detection in images. It consists in detecting the main bilateral symmetry axis inside artificial or natural images. State-of-the-art methods combine feature point detection, pairwise comparison and voting in Hough-like space. In spite of their good performance, they fail to give reliable results over challenging real-world and artistic images. In this paper, we propose a novel symmetry detection method using multi-scale edge features combined with local orientation histograms. An experimental evaluation is conducted on public datasets plus a new aesthetic-oriented dataset. The results show that our approach outperforms all other concurrent methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
- 4.
All PSU output with detected symmetry axes can be found in: http://perso.univ-st-etienne.fr/em68594h/SupplementalFilesPSU.zip.
- 5.
- 6.
Source code to generate AVA images and their symmetry labels: http://perso.univ-st-etienne.fr/em68594h/SymAVA.zip.
- 7.
All AVA output with the detected symmetry axes can be found in: http://perso.univ-st-etienne.fr/em68594h/SupplementalFilesAVA.zip.
References
Atadjanov, I., Lee, S.: Bilateral symmetry detection based on scale invariant structure feature. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 3447–3451. IEEE (2015)
Bairagi, V.: Symmetry-based biomedical image compression. J. Digital Imaging 28, 718–726 (2015)
Cai, D., Li, P., Su, F., Zhao, Z.: An adaptive symmetry detection algorithm based on local features. In: 2014 IEEE Visual Communications and Image Processing Conference, pp. 478–481. IEEE (2014)
Cho, M., Lee, K.M.: Bilateral symmetry detection via symmetry-growing. In: BMVC, pp. 1–11. Citeseer (2009)
Cicconet, M., Geiger, D., Gunsalus, K.C., Werman, M.: Mirror symmetry histograms for capturing geometric properties in images. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2981–2986. IEEE (2014)
Duda, R.O., Hart, P.E.: Use of the hough transformation to detect lines and curves in pictures. Commun. ACM 15(1), 11–15 (1972)
Kondra, S., Petrosino, A., Iodice, S.: Multi-scale kernel operators for reflection and rotation symmetry: further achievements. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 217–222. IEEE (2013)
Liu, J., Slota, G., Zheng, G., Wu, Z., Park, M., Lee, S., Rauschert, I., Liu, Y.: Symmetry detection from realworld images competition 2013: summary and results. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 200–205. IEEE (2013)
Liu, Y., Hel-Or, H., Kaplan, C.S.: Computational Symmetry in Computer Vision and Computer Graphics. Now Publishers Inc., Boston (2010)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Loy, G., Eklundh, J.-O.: Detecting symmetry and symmetric constellations of features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 508–521. Springer, Heidelberg (2006). doi:10.1007/11744047_39
Michaelsen, E., Muench, D., Arens, M.: Recognition of symmetry structure by use of gestalt algebra. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 206–210. IEEE (2013)
Ming, Y., Li, H., He, X.: Symmetry detection via contour grouping. In: 2013 20th IEEE International Conference on Image Processing (ICIP), pp. 4259–4263. IEEE (2013)
Mo, Q., Draper, B.: Detecting bilateral symmetry with feature mirroring. In: CVPR 2011 Workshop on Symmetry Detection from Real World Images (2011)
Murray, N., Marchesotti, L., Perronnin, F.: AVA: a large-scale database for aesthetic visual analysis. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2408–2415. IEEE (2012)
Patraucean, V., von Gioi, R.G., Ovsjanikov, M.: Detection of mirror-symmetric image patches. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 211–216. IEEE (2013)
Rauschert, I., Brocklehurst, K., Kashyap, S., Liu, J., Liu, Y.: First symmetry detection competition: summary and results. Technical report CSE11-012, Department of Computer Science and Engineering, The Pennsylvania State University (2011)
Teo, C.L., Fermuller, C., Aloimonos, Y.: Detection and segmentation of 2D curved reflection symmetric structures. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1644–1652 (2015)
Wang, Z., Tang, Z., Zhang, X.: Reflection symmetry detection using locally affine invariant edge correspondence. IEEE Trans. Image Process. 24(4), 1297–1301 (2015)
Yang, L., Liu, J., Tang, X.: Depth from water reflection. IEEE Trans. Image Process. 24(4), 1235–1243 (2015)
Zhao, S., Gao, Y., Jiang, X., Yao, H., Chua, T.S., Sun, X.: Exploring principles-of-art features for image emotion recognition. In: Proceedings of the ACM International Conference on Multimedia, pp. 47–56. ACM (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Elawady, M., Barat, C., Ducottet, C., Colantoni, P. (2016). Global Bilateral Symmetry Detection Using Multiscale Mirror Histograms. In: Blanc-Talon, J., Distante, C., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2016. Lecture Notes in Computer Science(), vol 10016. Springer, Cham. https://doi.org/10.1007/978-3-319-48680-2_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-48680-2_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48679-6
Online ISBN: 978-3-319-48680-2
eBook Packages: Computer ScienceComputer Science (R0)