Concurrent Self-Organizing Maps for pattern classification

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.