A new neural network model for automatic generation of Gabor-like feature filters

The automatic selection of feature variables is a task of increasing interest in the field of pattern recognition. Neural models have recently been used for this purpose. Among other models, the adaptive-subspace SOM (ASSOM) stands out because of its simplicity and biological plausibility. However, the main drawback of its application in image processing systems is that a priori information is necessary to choose a suitable network size and topology in advance. This article introduces the adaptive-subspace growing cell structures (ASGCS) network, which corresponds to a further improvement of the ASSOM that overcomes its main drawbacks. The ASGCS network is described and some examples of automatic generation of Gabor-like feature filter are given