Pattern Recognition as a Human Centered non-Euclidean Problem

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Pattern Recognition as a Human Centered non-Euclidean Problem
Regularities in the world are human defined. Patterns in the observed phenomena are there because we define and recognize them as such. Automatic pattern recognition tries to bridge the gap between human judgment and measurements made by artificial sensors. This is done in two steps: representation and generalization. Traditional representations of real world objects to be recognized, like features and pixels, either neglect possibly significant aspects of the objects, or neglect their dependencies. We therefor reconsider human recognition and observe that it is based on our direct experience of similarity or dissimilarity of objects. Using these concepts, a pattern recognition system can be defined in a natural way by a pairwise comparison of objects. This results in the dissimilarity representation for pattern recognition. An analysis of dissimilarity measures optimized for performance shows that they tend to be non-Euclidean. The Euclidean vector spaces, traditionally used in patter...
Robert P. W. Duin
Added 12 Feb 2011
Updated 12 Feb 2011
Type Journal
Year 2010
Authors Robert P. W. Duin
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