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 useful representations of the objects to be recognized; secondly, a classification 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 classification 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 affected by many different sources of noise. We report some results that show how the proposed scheme can produce a very promising performance for the classification of company logos corrupted by noise.
classification of company logos artificial neural networks adaptive processing of data structures structured representation of graphical items contour tree algorithm