Adaptive graphical pattern recognition for the classification of company logos

When dealing with a pattern recognition task two major issues must be faced : firstly, a feature extraction technique has to be applied to extract usef ul representations of the objects to be recognized; secondly, a classificat ion algorithm must be devised in order to produce a class hypothesis once a pattern representation is given. Adaptive graphical pattern recognition is proposed as a new approach to face these two issues when neither a purely symbolic nor a purely sub-symbolic representation seems adequate for the pa tterns. This approach is based on appropriate structured representations of patterns which are, subsequently, processed by recursive neural networks, that can be trained to perform the given classification task using connecti onist-based learning algorithms. In the proposed framework, the joint role of the structured representation and learning makes it possible to face tas ks in which input patterns are affected by many different sources of noise. We report some results that show how the proposed scheme can produce a ver y promising performance for the classification of company logos corrupted b y noise. (C) 2001 Pattern Recognition Society. Published by Elsevier Scienc e Ltd. All rights reserved.