Abstract
Petroglyphs can be found on rock panels all over the world. The possibilities of digital photography and more recently various 3D scanning methods opened a new stage for the documentation and analysis of petroglyphs. The existing work on petroglyph shape similarity has largely avoided the questions of articulation, merged petroglyphs and potentially missing parts of petroglyphs. We aim at contributing to close this gap by applying a novel petroglyph shape descriptor based on the skeletal graph. Our contribution is twofold: First, we provide a real-world dataset of petroglyph shapes. Second, we propose a graph-based shape descriptor for petroglyphs. Comprehensive evaluations show, that the combination of the proposed descriptor with existing ones improves the performance in petroglyph shape similarity modeling.
Chapter PDF
Similar content being viewed by others
References
Aslan, C., Erdem, A., Erdem, E., Tari, S.: Disconnected skeleton: shape at its absolute scale. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(12), 2188–2203 (2008)
Bai, X., Latecki, L.: Path similarity skeleton graph matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(7), 1282–1292 (2008)
Bai, X., Latecki, L., Liu, W.Y.: Skeleton pruning by contour partitioning with discrete curve evolution. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(3), 449–462 (2007). 00228
Bai, X., Liu, W., Tu, Z.: Integrating contour and skeleton for shape classification. In: 2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), pp. 360–367. IEEE (2009)
Baseski, E., Erdem, A., Tari, S.: Dissimilarity between two skeletal trees in a context. Pattern Recognition 42(3), 370–385 (2009)
Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(4), 509–522 (2002)
Bounova, G., de Weck, O.: Overview of metrics and their correlation patterns for multiple-metric topology analysis on heterogeneous graph ensembles. Phys. Rev. E 85, 016117 (2012)
Demirci, M.F., van Leuken, R.H., Veltkamp, R.C.: Indexing through Laplacian spectra. Computer Vision and Image Understanding 110(3), 312–325 (2008)
Deufemia, V., Paolino, L., de Lumley, H.: Petroglyph recognition using self-organizing maps and fuzzy visual language parsing. In: 2012 IEEE 24th International Conference on Tools with Artificial Intelligence (ICTAI), vol. 1, pp. 852–859 (2012)
Deufemia, V., Paolino, L.: Combining unsupervised clustering with a non-linear deformation model for efficient petroglyph recognition. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Li, B., Porikli, F., Zordan, V., Klosowski, J., Coquillart, S., Luo, X., Chen, M., Gotz, D. (eds.) ISVC 2013, Part II. LNCS, vol. 8034, pp. 128–137. Springer, Heidelberg (2013)
Di Ruberto, C.: Recognition of shapes by attributed skeletal graphs. Pattern Recognition 37(1), 21–31 (2004)
Erdem, A., Tari, S.: A similarity-based approach for shape classification using Aslan skeletons. Pattern Recognition Letters 31(13), 2024–2032 (2010)
Gibert, J., Valveny, E., Bunke, H.: Graph embedding in vector spaces by node attribute statistics. Pattern Recogn. 45(9), 3072–3083 (2012)
Klein, P.N., Sebastian, T.B., Kimia, B.B.: Shape matching using edit-distance: an implementation. In: Proceedings of the Twelfth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2001, Society for Industrial and Applied Mathematics, Philadelphia, pp. 781–790 (2001)
Krish, K., Snyder, W.: A new accumulator-based approach to shape recognition. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Remagnino, P., Porikli, F., Peters, J., Klosowski, J., Arns, L., Chun, Y.K., Rhyne, T.-M., Monroe, L. (eds.) ISVC 2008, Part II. LNCS, vol. 5359, pp. 157–169. Springer, Heidelberg (2008)
Kropatsch, W.G., et al. (eds.): GbRPR 2013. LNCS, vol. 7877. Springer, Heidelberg (2013)
Latecki, L.J., Lakamper, R.: Shape similarity measure based on correspondence of visual parts. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10), 1185–1190 (2000)
Latecki, L.J., Lakamper, R., Eckhardt, T.: Shape descriptors for non-rigid shapes with a single closed contour. In: IEEE Conference on Computer Vision and Pattern Recognition, 2000, Proceedings, vol. 1, pp. 424–429. IEEE (2000)
Li, G., Semerci, M., Yener, B., Zaki, M.J.: Graph classification via topological and label attributes. In: 9th Workshop on Mining and Learning with Graphs (with SIGKDD) (August 2011) (2011). 00009
Ling, H., Jacobs, D.W.: Shape classification using the inner-distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(2), 286–299 (2007)
Loncaric, S.: A survey of shape analysis techniques. Pattern Recognition 31(8), 983–1001 (1998)
Mai, F., Chang, C.Q., Hung, Y.S.: Affine-invariant shape matching and recognition under partial occlusion. In: 2010 17th IEEE International Conference on Image Processing (ICIP), pp. 4605–4608. IEEE (2010)
Mokhtarian, F.: Silhouette-based isolated object recognition through curvature scale space. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(5), 539–544 (1995)
Mokhtarian, F., Abbasi, S., Kittler, J., et al.: Efficient and robust retrieval by shape content through curvature scale space. Series on Software Engineering and Knowledge Engineering 8, 51–58 (1997)
Mokhtarian, F., Mackworth, A.: Scale-based description and recognition of planar curves and two-dimensional shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(1), 34–43 (1986)
Pavlidis, T.: A review of algorithms for shape analysis. Computer graphics and image processing 7(2), 243–258 (1978)
Petrakis, E.G.M., Diplaros, A., Milios, E.: Matching and retrieval of distorted and occluded shapes using dynamic programming. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(11), 1501–1516 (2002)
Riesen, K., Emmenegger, S., Bunke, H.: A novel software toolkit for graph edit distance computation. In: Kropatsch, W.G., Artner, N.M., Haxhimusa, Y., Jiang, X. (eds.) GbRPR 2013. LNCS, vol. 7877, pp. 142–151. Springer, Heidelberg (2013)
Seidl, M., Breiteneder, C.: Automated petroglyph image segmentation with interactive classifier fusion. In: Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP 2012) (2012)
Seidl, M., Judmaier, P., Baker, F., Chippindale, C., Egger, U., Jax, N., Weis, C., Grubinger, M., Seidl, G.: Multi-touch rocks: playing with tangible virtual heritage in the museum - first user tests. In: VAST11: The 12th International Symposium on Virtual Reality, Archaeology and Intelligent Cultural Heritage - Short Papers, pp. 73–76 (2011)
Siddiqi, K., Kimia, B.: A shock grammar for recognition. In: 1996 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1996, Proceedings CVPR 1996, pp. 507–513 (1996)
Siddiqi, K., Shokoufandeh, A., Dickinson, S., Zucker, S.: Shock graphs and shape matching. International Journal of Computer Vision 35(1), 13–32 (1999)
Sun, K.B., Super, B.J.: Classification of contour shapes using class segment sets. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, CVPR 2005, vol. 2, pp. 727–733. IEEE (2005)
Xu, Y., Wang, B., Liu, W., Bai, X.: Skeleton graph matching based on critical points using path similarity. In: Zha, H., Taniguchi, R., Maybank, S. (eds.) ACCV 2009, Part III. LNCS, vol. 5996, pp. 456–465. Springer, Heidelberg (2010)
Yang, M., Kpalma, K., Ronsin, J.: A survey of shape feature extraction techniques. Pattern Recognition 43–90 (2008). 00163
Zeng, Z., Tung, A.K., Wang, J., Feng, J., Zhou, L.: Comparing stars: On approximating graph edit distance. Proceedings of the VLDB Endowment 2(1), 25–36 (2009)
Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recognition 37(1), 1–19 (2004)
Zhu, Q., Wang, X., Keogh, E., Lee, S.H.: Augmenting the generalized Hough transform to enable the mining of petroglyphs. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2009, pp. 1057–1066. ACM, New York (2009)
Zhu, Q., Wang, X., Keogh, E., Lee, S.H.: An efficient and effective similarity measure to enable data mining of petroglyphs. Data Mining and Knowledge Discovery 23(1), 91–127 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Seidl, M., Wieser, E., Zeppelzauer, M., Pinz, A., Breiteneder, C. (2015). Graph-Based Shape Similarity of Petroglyphs. In: Agapito, L., Bronstein, M., Rother, C. (eds) Computer Vision - ECCV 2014 Workshops. ECCV 2014. Lecture Notes in Computer Science(), vol 8925. Springer, Cham. https://doi.org/10.1007/978-3-319-16178-5_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-16178-5_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16177-8
Online ISBN: 978-3-319-16178-5
eBook Packages: Computer ScienceComputer Science (R0)