Effect of grouping in vector recognition system based on SOM

Abstract

This paper discusses effect of grouping on vector classifiers that are based on self-organising map (SOM). The SOM is an unsupervised learning neural network, and is used to form clusters using its topology preserving nature. Thus it is used for various pattern recognition applications. In image recognition, recognition accuracy is degraded under difficult lighting conditions. This paper proposes a new image recognition system that employs a grouping method. The proposed system does the grouping of vectors according to their brightness, and multiple vector classifiers are assigned to every groups. Recognition parameters of each classifier are tuned for the vectors belonging to its group. The proposed method is applied to position identification from images obtained from an on-board camera on a mobile robot. Comparison between the recognition systems with and without the grouping shows that the grouping can improve recognition accuracy.