Database Performance Tuning and Optimization (PDF)

2 Stochastic Shape Theory (p. 75)

Christian Cenker
Georg Pflug
Manfred Mayer

Stochastic models and statistical procedures are essential for pattern recognition. Linear discriminant analysis, parametric and nonparametric density estimation, maximumlikelihood classi.cation, supervised and nonsupervised learning, neural nets, parametric, nonparametric, and fuzzy clustering, principal component analysis, simulated annealing are only some of the well-known statistical techniques used for pattern recognition. Markov models and other stochastic models are often used to describe statistical characteristics of patterns in the pattern space.

We want to concentrate on modeling and feature extraction using new techniques.We do not model the characteristics of the pattern space but the generation of the patterns, i.e., modeling the pattern generation process via stochastic processes. Furthermore, wavelets and wavelet packets will help us to construct a feature extractor. Applying our models to a sample application we noticed the lack of global non-linear optimization algorithms. Thus, we added a section on optimization, in which we present a modification of a multi-level single-linkage technique that can be used in high-dimensional feature spaces.

2.1 Shape Analysis

A project on o.ine signature verfication shows the need for new approaches. Standard methods do not show the wanted accuracy, nevertheless, they have been implemented at a first stage in order to compare the results. As all signatures of one person are of di.erent but similar shape we look for a description of the similarity and the di.erence. First, a signature is a special form of curve, we discard all color, thickness and "pressure" information from the scanned signature (cf. (AYF86)), leaving only a thinned polygonal shape. We have a connected skeleton of the "contour". The .rst problem to solve is the parameterization of the curve, i.e., to get a onedimensional function