Petroglyph Recognition Using Self-Organizing Maps and Fuzzy Visual Language Parsing

Petroglyphs are images carved into a rock surface by prehistoric people using a symbolic or ritual language. Although they constitute an historical patrimony of inestimable value, little efforts have been devoted to the development of automated tools for their classification and interpretation. In this work we present a new algorithm for recognizing petroglyphs within scenes composed of several engraved figures. The proposal combines an unsupervised recognizer, Self-Organizing Maps (SOM), with a fuzzy visual language parser. The first classifies the petroglyph symbols extracted from a scene by using Radon transform as shape descriptor. The latter exploits the archeological knowledge about recurring patterns within scenes to solve ambiguous interpretations. The algorithm has been evaluated on a set of 50 petroglyph scenes, containing about 500 carved symbols from Mount Bego rock art site, and achieved very promising results.

[1]  Viviana Mascardi,et al.  Rock Art Interpretation within Indiana MAS , 2012, KES-AMSTA.

[2]  Christian Breiteneder,et al.  Detection and Classification of Petroglyphs in Gigapixel Images - Preliminary Results , 2011, VAST.

[3]  James F. Allen.  Maintaining knowledge about temporal intervals , 1983, CACM.

[4]  Gennaro Costagliola,et al.  Visual language implementation through standard compiler-compiler techniques , 2007, J. Vis. Lang. Comput..

[5]  Nadeem Salamat,et al.  Two-Dimensional Fuzzy Spatial Relations: A New Way of Computing and Representation , 2012, Adv. Fuzzy Syst..

[6]  Andrew P. Paplinski,et al.  Rotation Invariant Categorization of Visual Objects Using Radon Transform and Self-Organizing Modules , 2010, ICONIP.

[7]  Shinji Mizuno,et al.  Shape Analysis of Petroglyphs in Central Asia , 2006 .

[8]  Eamonn J. Keogh,et al.  An efficient and effective similarity measure to enable data mining of petroglyphs , 2010, Data Mining and Knowledge Discovery.

[9]  Filomena Ferrucci,et al.  Constructing meta-CASE workbenches by exploiting visual language generators , 2006, IEEE Transactions on Software Engineering.

[10]  K. I. Tashbaeva,et al.  Petroglyphs of Central Asia , 2001 .

[11]  Luca Paolino,et al.  Supporting the on-site emergency management through a visualisation technique for mobile devices , 2010, J. Locat. Based Serv..

[12]  Henry de Lumley,et al.  Les gravures rupestres du Chalcolithique et de l’âge du Bronze ancien de la région du mont Bego. Les mythes cosmogoniques des premiers peuples métallurgistes des Alpes méridionales , 2009 .

[13]  Steven Salzberg,et al.  On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach , 1997, Data Mining and Knowledge Discovery.

[14]  Cordelia Schmid,et al.  Selection of scale-invariant parts for object class recognition , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[15]  Tobias Meisen,et al.  Efficient similarity search using the Earth Mover's Distance for large multimedia databases , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[16]  Michele Risi,et al.  Sketch Grammars: a formalism for describing and recognizing diagrammatic sketch languages , 2005, Eighth International Conference on Document Analysis and Recognition (ICDAR'05).

[17]  Christopher Chippindale.  Clarence Bicknell: archaeology and science in the 19th century , 1984, Antiquity.

[18]  Eamonn J. Keogh,et al.  Augmenting the generalized hough transform to enable the mining of petroglyphs , 2009, KDD.

[19]  Michele Risi,et al.  Building syntax-aware editors for visual languages , 2005, J. Vis. Lang. Comput..

[20]  Viviana Mascardi,et al.  An agent-based framework for sketched symbol interpretation , 2008, J. Vis. Lang. Comput..

[21]  Camille Jullian.  C. Bicknell, A guide to the prehistoric Rock Engravings in the Italian Maritime Alps, 1913 , 1915 .