Concurrent self-organizing maps for pattern classification

Abstract

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 the 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.