Adaptive graphical pattern recognition for the classification of company logos

When dealing with a pattern recognition task two major issues must be faced: "rstly, a feature extraction technique has to be applied to extract useful representations of the objects to be recognized; secondly, a classi"cation 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 patterns. 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 classi"cation task using connectionist-based learning algorithms. In the proposed framework, the joint role of the structured representation and learning makes it possible to face tasks in which input patterns are a!ected by many di!erent sources of noise. We report some results that show how the proposed scheme can produce a very promisingperformancefortheclassi"cationofcompanylogoscorruptedbynoise. 2001PatternRecognitionSociety. Published by Elsevier Science Ltd. All rights reserved.