Layered Self-Organizing Map for Image Classification in Unrestricted Domains

  • Christian O’Connell
  • Andrea Kutics
  • Akihiko Nakagawa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8156)


The inherent difficultly in unrestricted image domain classification is due to the many different features exhibited by images. Efforts made toward classification of abstract features tend to focus on a single attribute. Without a method of unifying descriptors, it becomes very difficult to perform multi-feature analysis. Extending the concept of the Self-Organizing Feature Map to include multiple competitive layers, it has been possible to create a new type of Artificial Neural Network capable of analyzing image and signal datasets with multiple feature descriptors concurrently in a powerful yet computationally light manner. Compared to standard CBIR retrieval approach, a marked increase in the precision of clustering of 13 points has been achieved, along with a reduction in computation time.


self-organizing map image classification features image retrieval 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Christian O’Connell
    • 1
  • Andrea Kutics
    • 2
  • Akihiko Nakagawa
    • 2
    • 3
  1. 1.University of EssexColchesterUnited Kingdom
  2. 2.International Christian University (ICU)TokyoJapan
  3. 3.University of Electro-Communications (UEC)TokyoJapan

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