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. 

(1) This presentation is based on joint work with Elzbieta Pekalska,  University of Manchester, UK. It was also presented as a key-note speech at the CIARP 2011, 15-17 November 2011, Pucon Chili and printed in its proceedings.