Image Clustering Method Based on Density Maps Derived from Self-Organizing Mapping: SOM

A new method for image clustering with density maps derived from Self-Organizing Maps (SOM) is proposed together with a clarification of learning processes during a construction of clusters. It is found that the proposed SOM based image clustering method shows much better clustered result for both simulation and real satellite imagery data. It is also found that the separability among clusters of the proposed method is 16% longer than the existing k-mean clustering. It is also found that the separability among clusters of the proposed method is 16% longer than the existing k-mean clustering. In accordance with the experimental results with Landsat-5 TM image, it takes more than 20000 of iteration for convergence of the SOM learning processes.

@article{Arai2012,
  title     = {Image Clustering Method Based on Density Maps Derived from Self-Organizing Mapping: SOM},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {3},
  number    = {7},
  year      = {2012},
  publisher = {The Science and Information Organization},
  author    = {Kohei Arai},
  doi       = {10.14569/IJACSA.2012.030714},
  url       = {https://doi.org/10.14569/IJACSA.2012.030714}
}