Digital Signal Processing Using Matlab

13.1. Theoretical background

13.1.1. Introduction

Pattern recognition, one of the most important aspects of artificial intelligence, is an appropriate field for the development, validation and comparison of different learning techniques: statistical or structural, supervised or unsupervised, inductive or deductive, etc.

Patterns are general concepts describing an object (mechanical part, obstacle, human face, etc.) or a phenomenon (disease, system operating state, emotion, etc.). They are provided by a sensor or transducer to a recognition system in the form of a data set. The classification is possible only if characteristic information about the observed pattern is confined in this data set, which is then called a pattern signature or fingerprint.

Since the signatures are generally measured, they have a random nature. Thus, the statistical approach, which is described in this chapter, is the most used for pattern recognition. More precisely, we consider the supervised learning framework, which requires a database. This database contains labeled patterns belonging to the M predefined classes ω₁ ω₂, ..., ω_M. These patterns are repeatedly presented to the classifier in order to derive a decision rule optimizing a given criterion.

The general structure of a supervised statistical classification chain is presented in Figure 13.1. This process aims at classifying any unknown pattern using its signature and consists of the following steps:

– feature vector extraction from the recorded ...