A clustering method using hierarchical self-organizing maps

被引:10
作者
Endo, M [1 ]
Ueno, M [1 ]
Tanabe, T [1 ]
机构
[1] NTT Cyber Space Labs, Tokyo 1808585, Japan
来源
JOURNAL OF VLSI SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY | 2002年 / 32卷 / 1-2期
关键词
image retrieval system; clustering; self-organizing maps; hierarchical SOM; cooperative learning algorithm; neighborhood function;
D O I
10.1023/A:1016371519687
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 [计算机科学与技术];
摘要
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.
引用
收藏
页码:105 / 118
页数:14
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