On Fast Non-metric Similarity Search by Metric Access Methods

  • Tomáš Skopal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3896)


The retrieval of objects from a multimedia database employs a measure which defines a similarity score for every pair of objects. The measure should effectively follow the nature of similarity, hence, it should not be limited by the triangular inequality, regarded as a restriction in similarity modeling. On the other hand, the retrieval should be as efficient (or fast) as possible. The measure is thus often restricted to a metric, because then the search can be handled by metric access methods (MAMs). In this paper we propose a general method of non-metric search by MAMs. We show the triangular inequality can be enforced for any semimetric (reflexive, non-negative and symmetric measure), resulting in a metric that preserves the original similarity orderings (retrieval effectiveness). We propose the TriGen algorithm for turning any black-box semimetric into (approximated) metric, just by use of distance distribution in a fraction of the database. The algorithm finds such a metric for which the retrieval efficiency is maximized, considering any MAM.


Near Neighbor Range Query Access Method Query Object Triangular Inequality 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tomáš Skopal
    • 1
  1. 1.FMP, Department of Software EngineeringCharles University in PraguePrague 1Czech Republic

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