ASGCS: A new self-organizing network for automatic selection of feature variables
The automatic selection of invariant feature variables is very important in pattern recognition systems. Recently, neural models have begun to be employed for this task. Among other models the ASSOM stands out because of its simplicity and biological plausibility. However, the main drawback of the application of the ASSOM in image processing systems is that a priori information is necessary to choose a suitable network size and topology in advance. The main purpose of this article is to present 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 introduces some GCS (Growing Cell Structures) concepts into the ASSOM model. The ASGCS network is described and some examples of automatic Gabor-like feature filter generation are given.
KeywordsAdaptive-Subspace Self-Organizing Map (ASSOM) Growing Cell Structures (GCS) Adaptive-Subspace Growing Cell Structures (ASGCS) Gabor-Filters Automatic features extraction
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