Abstract
Petroglyphs are prehistoric engravings in stone unrevealing stories of ancient life and describing a conception of the world transmitted till today. In the current paper we consider the problem of developing tools that automate their recognition. This is a challenging problem mainly due to the high level of distortion and variability of petroglyph reliefs. To address these issues, we propose a two-stage approach that combines unsupervised clustering, for quickly obtaining a raw classification of the query image, and a non-linear deformation model, for accurately evaluating the shape similarity between the query and the images of the more appropriate classes.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Belongie, S., Malik, J., Puzicha, J.: Shape Matching and Object Recognition Using Shape Contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 509–521 (2002)
Bicknell, C.M.: A Guide to the Prehistoric Rock Engravings in the Italian Maritime Alps. Tip. G. Bessone (1913)
Chippindale, C., Bicknell, C.: Archaeology and Science in the 19th Century. Antiquity 58, 185–193 (1984)
de Lumley, H., Echassoux, A.: The Rock Carvings of the Chalcolithic and Ancient Bronze Age from the Mont Bego Area. The Cosmogonic Myths of the Early Metallurgic Settlers in the Southern Alps. L’Anthropologie 113, 969–1004 (2009)
Deselaers, T., Weyand, T., Keysers, D., Macherey, W., Ney, H.: Fire in ImageCLEF 2005: Combining content-based image retrieval with textual information retrieval. In: Peters, C., Gey, F.C., Gonzalo, J., Müller, H., Jones, G.J.F., Kluck, M., Magnini, B., de Rijke, M., Giampiccolo, D. (eds.) CLEF 2005. LNCS, vol. 4022, pp. 652–661. Springer, Heidelberg (2006)
Deufemia, V., Paolino, L., de Lumley, H.: Petroglyph Recognition using Self-Organizing Maps and Fuzzy Visual Language Parsing. In: Proceedings of the IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2012, pp. 852–859. IEEE (2012)
Deufemia, V., Paolino, L., Tortora, G., Traverso, A., Mascardi, V., Ancona, M., Martelli, M., Bianchi, N., de Lumley, H.: Investigative Analysis Across Documents and Drawings: Visual Analytics for Archaeologists. In: Proceedings of the International Working Conference on Advanced Visual Interfaces, AVI 2012, pp. 539–546. ACM (2012)
Keysers, D., Deselaers, T., Gollan, C., Ney, H.: Deformation Models for Image Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 1422–1435 (2007)
Keysers, D., Gollan, C., Ney, H.: Local Context in Non-linear Deformation Models for Handwritten Character Recognition. In: Proceedings of International Conference on Pattern Recognition, ICPR 2004, pp. 511–514. IEEE (2004)
Kohonen, T., Schroeder, M.R., Huang, T.S. (eds.): Self-Organizing Maps, 3rd edn. Springer-Verlag New York, Inc., USA
Mascardi, V., Deufemia, V., Malafronte, D., Ricciarelli, A., Bianchi, N., de Lumley, H.: Rock art interpretation within Indiana MAS. In: Jezic, G., Kusek, M., Nguyen, N.-T., Howlett, R.J., Jain, L.C. (eds.) KES-AMSTA 2012. LNCS, vol. 7327, pp. 271–281. Springer, Heidelberg (2012)
Mascardi, V., Briola, D., Locoro, A., Grignani, D., Deufemia, V., Paolino, L., Bianchi, N., de Lumley, H., Malafronte, D., Ricciarelli, A.: A Holonic Multi-Agent System for Sketch, Image and Text Interpretation in the Rock Art Domain. International Journal of Innovative Computing Information and Control 1 (2014)
Paolino, L., Sebillo, M., Tortora, G., Vitiello, G., Laurini, R.: Phenomena - A Visual Environment for Querying Heterogeneous Spatial Data. Journal of Visual Languages and Computing 20, 420–436 (2009)
Petroannotator, http://www.cs.ucr.edu/~qzhu/papers/CAPTCHA/PetroAnnotator/
Petroglyphs, http://en.wikipedia.org/wiki/Petroglyph (last accessed June 28, 2013)
Salzberg, S.L.: On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach. Data Min. Knowl. Discov. 1, 317–328 (1997)
Seidl, M., Breiteneder, C.: Detection and Classification of Petroglyphs in Gigapixel Images - Preliminary Results. In: Proceedings of the 12th International Symposium on Virtual Reality, Archaeology and Intelligent Cultural Heritage, VAST 2011, pp. 45–48. Eurographics Association (2011)
Sher, Y.A.: Petroglyphs in Central Asia. Nauka (1980) (in Russian)
Springmann, M., Dander, A., Schuldt, H.: Improving Efficiency and Effectiveness of the Image Distortion Model. Pattern Recognit Letters 29, 2018–2024 (2008)
Takaki, R., Toriwaki, J., Mizuno, S., Izuhara, R., Khudjanazarov, M., Reutova, M.: Shape analysis of petroglyphs in central Asia. Forma 21, 243–258 (2006)
Zhu, Q., Wang, X., Keogh, E., Lee, S.-H.: Augmenting the Generalized Hough Transform to Enable the Mining of Petroglyphs. In: Proceedings of the KDD 2009, pp. 1057–1066 (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, 91–127 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Deufemia, V., Paolino, L. (2013). Combining Unsupervised Clustering with a Non-linear Deformation Model for Efficient Petroglyph Recognition. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2013. Lecture Notes in Computer Science, vol 8034. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41939-3_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-41939-3_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41938-6
Online ISBN: 978-3-642-41939-3
eBook Packages: Computer ScienceComputer Science (R0)