Concurrent Self-Organizing Maps for pattern classification

被引：43

作者：

Neagoe, VE ^{[1
]}

Ropot, AD ^{[1
]}

机构：

[1] POLITEHNICA Univ Bucharest, Dept Appl Elect & Informat Eng, Bucharest 77206, Romania

来源：

FIRST IEEE INTERNATIONAL CONFERENCE ON COGNITIVE INFORMATICS, PROCEEDINGS | 2002年

关键词：

D O I：

10.1109/COGINF.2002.1039311

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a new neural classification model called Concurrent Self-Organizing Maps (CSOM), representing a winner-takes-all collection of small SOM networks. Each SOM of the system is trained individually to provide best results for one class only. We have considered two significant applications: face recognition and multispectral satellite image classification. For first application, we have used the ORL database of 400 faces (40 classes). With CSOM (40 small linear SOMs), we have obtained a recognition score of 91%, while using a single big SOM one obtains a score of 83.5% only! For second application, we have classified the multispectral pixels belonging to a LANDSAT TM image with 7 bands into seven thematic categories. The experimental results lead to the recognition rate of 95.29% using CSOM (7 circular SOMs), while with a single big SOM, one obtains a 94.31% recognition rate. Simultaneously, CSOM leads to a significant reduction of training time by comparison to SOM.

引用

页码：304 / 312

页数：9

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