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)


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.


Feature Space Average Precision Interest Point Class Distribution Semantic Class 
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  1. 1.
    Hauptmann, A.G., Christel, M.G., Yan, R.: Video retrieval based on semantic concepts. Proceedings of the IEEE 96(4), 602–622 (2008)CrossRefGoogle Scholar
  2. 2.
    Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer Series in Information Sciences, vol. 30. Springer, Berlin (2001)CrossRefzbMATHGoogle Scholar
  3. 3.
    Laaksonen, J., Koskela, M., Oja, E.: Class distributions on SOM surfaces for feature extraction and object retrieval. Neural Networks 17(8-9), 1121–1133 (2004)CrossRefGoogle Scholar
  4. 4.
    Laaksonen, J., Koskela, M., Oja, E.: PicSOM—Self-organizing image retrieval with MPEG-7 content descriptions. IEEE Transactions on Neural Networks, Special Issue on Intelligent Multimedia Processing 13(4), 841–853 (2002)CrossRefzbMATHGoogle Scholar
  5. 5.
    Kohonen, T., Kaski, S., Lagus, K., Salojärvi, J., Honkela, J., Paatero, V., Saarela, A.: Self organization of a massive text document collection. IEEE Transactions on Neural Networks 11(3), 574–585 (2000)CrossRefGoogle Scholar
  6. 6.
    Pampalk, E., Rauber, A., Merkl, D.: Using smoothed data histograms for cluster visualization in self-organizing maps. In: Dorronsoro, J.R. (ed.) ICANN 2002. LNCS, vol. 2415, pp. 871–876. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  7. 7.
    Koskela, M., Laaksonen, J., Oja, E.: Implementing relevance feedback as convolutions of local neighborhoods on self-organizing maps. In: Dorronsoro, J.R. (ed.) ICANN 2002. LNCS, vol. 2415, pp. 981–986. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  8. 8.
    Koikkalainen, P.: Progress with the tree-structured self-organizing map. In: 11th European Conference on Artificial Intelligence, European Committee for Artificial Intelligence (ECCAI) (August 1994)Google Scholar
  9. 9.
    van de Sande, K.E.A., Gevers, T., Snoek, C.G.M.: Evaluation of color descriptors for object and scene recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska, USA (June 2008)Google Scholar
  10. 10.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  11. 11.
    ISO/IEC: Information technology - Multimedia content description interface - Part 3: Visual, 15938-3:2002(E) (2002)Google Scholar
  12. 12.
    Manning, C., Schütze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)zbMATHGoogle Scholar

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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