VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Communications of the ACM
Self-organizing maps
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Learing Fine Motion by Using the Hierarchical Extended Kohonen Map
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Hierarchical overlapped SOM's for pattern classification
IEEE Transactions on Neural Networks
Hierarchical SOMs: Segmentation of Cell-Migration Images
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
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We describe a method of clustering that uses self-organizing maps (SOMs) in a method of image classification. To ensure that this clustering method is fast, we defined a hierarchical SOM and used it to construct the clustering method (M. Endo, M. Ueno, T. Tanabe, and M. Yamamoto, in Proc. of the IEEE Int. Workshop on Neural Networks for Signal Processing X, 2000, pp. 261–270). We define the clustering method in detail and outline its behavior as determined on the basis of both theory and experiment. We also propose a cooperative learning algorithm for the hierarchical SOM. Experiments on artificial image data confirmed the basic performance and adaptability of the SOM in clustering images. We also confirmed, both experimentally and theoretically, that our method is faster SOM, for the objects used in these experiments, than a method based on a non-hierarchical SOM.