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The dissimilarity representation for structural pattern recognition

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03 Apr

The dissimilarity representation for structural pattern recognition

by Telma João
in
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The patterns in collections of real world objects are often not based on a limited set of isolated properties such as features. Instead, the totality of their appearance constitutes the basis of the human recognition of patterns. Structural pattern recognition aims to find explicit procedures that mimic the learning and classification made by human experts in well-defined and restricted areas of application.

This is often done by defining dissimilarity measures between objects and measuring them between training examples and new objects to be recognized.

The dissimilarity representation offers the possibility to apply the tools developed in machine learning and statistical pattern recognition to learn from structural object representations such as graphs and strings. These procedures are also applicable to the recognition of histograms, spectra, images and time sequences taking into account the connectivity of samples (bins, wavelengths, pixels or time samples). An additional property of this representation is that it can easily include out-of-training-set observations, making it an ideal tool for context dependent recognition.

The topic of dissimilarity representation is related to the field of non-Mercer kernels in machine learning but it covers a wider set of classifiers and applications. Recently much progress has been made in this area and many interesting applications have been studied in medical diagnosis, seismic and hyperspectral imaging, chemometrics and computer vision. This presentation offers an introduction to this field and includes a number of real world applications.

Robert P.W. Duin

Delft University of Technology, The Netherlands


Robert P.W. Duin received in 1978 the Ph.D. degree in applied physics from Delft University of Technology, Delft, The Netherlands, for a thesis on statistical pattern recognition. He is currently an Associate Professor in the Faculty of Electrical Engineering, Mathematics and Computer Science of the same university. During 1980-1990, he studied and developed hardware architectures and software configurations for interactive image analysis. After that he became involved with pattern recognition by neural networks. His current research interests are in the design, evaluation, and application of algorithms that learn from examples, which includes neural network classifiers, support vector machines, classifier combining strategies, and one-class classifiers. Especially complexity issues and the learning behavior of trainable systems receive much interest. From 2000 he started to investigate alternative object representations for classification and he thereby became interested in dissimilarity-based pattern recognition, trainable similarities, and the handling of non-Euclidean data. Dr. Duin is an associated editor of Pattern Recognition Letters and a past-associate editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence. He is a Fellow of the International Association for Pattern Recognition (IAPR). In August 2006 he was the recipient of the Pierre Devijver Award for his contributions to statistical pattern recognition.