Concurrent self-organizing maps for pattern classification

Abstract

We present a new neural classification model calledConcurrent 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.

Analysis of unsupervised learning techniques for face recognition

“…Self-organizing map (SOM) (Kohonen, 1988(Kohonen, , 1997) is another algorithm used for self-organization or unsupervised learning that discovers the significant features in the input data. These have also been successfully used as a way of dimensionality reduction and feature selection for face space representations (Lawrence et al, 1996;Neagoe and Ropot, 2002;Tan et al, 2005).…”

Privacy-Preserving Clustering on Distributed Databases: A Review and Some Contributions

“…During the classifier utilization, the map which presents the lesser quantified error is declared winner and its index is the index of the class to which the pattern belongs. In tests performed with CSOM model, the authors consider three applications in which this model presents fair results: face recognition, speech recognition and multi-spectral satellite images (Neagoe & Ropot, 2002;Neagoe & Ropot, 2004). Arroyave et al (2002) present a parallel implementation of multiple SOM networks using a Beowulf cluster, with application on the organization of text files.…”

Face Recognition Using Self-Organizing Maps

“…This system was tested on the ORL database, and resulted in a correct recognition rate of 96.2% for the case of a training set, including five faces per person and a test set including five faces per person. Neagoe (Neagoe & Ropot, 2002) 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.…”