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Optimal Combination of SOM Search in Best-Matching Units and Map Neighborhood

  • Mats Sjöberg
  • Jorma Laaksonen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5629)

Abstract

The distribution of a class of objects, such as images depicting a specific topic, can be studied by observing the best-matching units (BMUs) of the objects’ feature vectors on a Self-Organizing Map (SOM). When the BMU “hits” on the map are summed up, the class distribution may be seen as a two-dimensional histogram or discrete probability density. Due to the SOM’s topology preserving property, one is motivated to smooth the value field and spread out the values spatially to neighboring units, from where one may expect to find further similar objects. In this paper we study the impact of using more map units than just the single BMU of each feature vector in modeling the class distribution. We demonstrate that by varying the number of units selected in this way and varying the width of the spatial convolution one can find an optimal combination which maximizes the class detection performance.

Keywords

Feature Space Average Precision Interest Point Class Distribution Semantic Class 
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 2009

Authors and Affiliations

  • Mats Sjöberg
    • 1
  • Jorma Laaksonen
    • 1
  1. 1.Department of Information and Computer ScienceHelsinki University of Technology TKKTKKFinland

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