An efficient and effective similarity measure to enable data mining of petroglyphs

  • Authors:

  • Qiang Zhu;Xiaoyue Wang;Eamonn Keogh;Sang-Hee Lee

  • Affiliations:

  • Department of Computer Science and Engineering, University of California Riverside, Riverside, USA 92521;Department of Computer Science and Engineering, University of California Riverside, Riverside, USA 92521;Department of Computer Science and Engineering, University of California Riverside, Riverside, USA 92521;Department of Anthropology, University of California Riverside, Riverside, USA 92521

  • Venue:
  • Data Mining and Knowledge Discovery
  • Year:
  • 2011

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Abstract

Rock art is an archaeological term for human-made markings on stone, including carved markings, known as petroglyphs, and painted markings, known as pictographs. It is believed that there are millions of petroglyphs in North America alone, and the study of this valued cultural resource has implications even beyond anthropology and history. Surprisingly, although image processing, information retrieval and data mining have had a large impact on many human endeavors, they have had essentially zero impact on the study of rock art. In this work we identify the reasons for this, and introduce a novel distance measure and algorithms which allow efficient and effective data mining of large collections of rock art.