A Topology-Independent Similarity Measure for High-Dimensional Feature Spaces

  • Jochen Kerdels
  • Gabriele Peters
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4669)


In the field of computer vision feature matching in high dimensional feature spaces is a commonly used technique for object recognition. One major problem is to find an adequate similarity measure for the particular feature space, as there is usually only little knowledge about the structure of that space. As a possible solution to this problem this paper presents a method to obtain a similarity measure suitable for the task of feature matching without the need for structural information of the particular feature space. As the described similarity measure is based on the topology of the feature space and the topology is generated by a growing neural gas, no knowledge about the particular structure of the feature space is needed. In addition, the used neural gas quantizes the feature vectors and thus reduces the amount of data which has to be stored and retrieved for the purpose of object recognition.


Feature Vector Similarity Measure Feature Space Object Recognition Distance Matrix 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Jochen Kerdels
    • 1
  • Gabriele Peters
    • 2
  1. 1.DFKI - German Research Center for Artificial Intelligence, Robotics Lab, Robert Hooke Str. 5, D-28359 BremenGermany
  2. 2.University of Dortmund, Department of Computer Science, Computer Graphics, Otto-Hahn-Str. 16, D-44221 DortmundGermany

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