Approximation-Based Similarity Search for 3-D Surface Segments

  • Authors:
  • Hans-Peter Kriegel;Thomas Seidl

  • Affiliations:
  • University of Munich, Institute for Computer Science, Oettingenstr. 67, D-80538 München, Germany kriegel@dbs.informatik.uni-muenchen.de;University of Munich, Institute for Computer Science, Oettingenstr. 67, D-80538 München, Germany kriegel@dbs.informatik.uni-muenchen.de

  • Venue:
  • Geoinformatica
  • Year:
  • 1998

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Abstract

The issue of finding similar 3-D surface segments arises in manyrecent applications of spatial database systems, such as molecular biology,medical imaging, CAD, and geographic information systems. Surface segmentsbeing similar in shape to a given query segment are to be retrieved from thedatabase. The two main questions are how to define shape similarity and howto efficiently execute similarity search queries. We propose a newsimilarity model based on shape approximation by multi-parametric surfacefunctions that are adaptable to specific application domains. We then defineshape similarity of two 3-D surface segments in terms of their mutualapproximation errors. Applying the multi-step query processing paradigm, wepropose algorithms to efficiently support complex similarity search queriesin large spatial databases. A new query type, called the ellipsoid query, isutilized in the filter step. Ellipsoid queries, being specified by quadraticforms, represent a general concept for similarity search. Our majorcontribution is the introduction of efficient algorithms to performellipsoid queries on multidimensional index structures. Experimental resultson a large 3-D protein database containing 94,000 surface segmentsdemonstrate the successful application and the high performance of our method.