Other important parameters exist that are specific to each segmentation method,

explored in the following sections.

2.2.1 Classical Sequential Supervised Approach

 The first step for understanding segmentation is to understand supervised change

detection in a signal presenting switching among a finite number K of known processes.

In that case, predictive models are available (or developed for each process using

measured time series), and represented by their PDF's pk(X(n)), X(n) being the history

of the measurements x(n) over n time steps.

 Then the time series is monitored online by computing at every step for any two

processes the log-likelihood ratio of the processes PDF's values at that time step, as

shown in (2.6).


 L,(n)= log X()) with i,j=1....K (2.6)
 pj (X(n))

 The log-likelihood ratio is a very important tool in sequential change point

detection. Indeed, it serves as the decision function: basically, once a starting regime i has

been identified, all L4 (n) are monitored for j # i, and whenever one L4 (n) goes above a

set threshold, it means there has been a change of regime at that point. This method needs

therefore offline training of the experts, and then an online segmentation is performed.

2.2.2 Classical Sequential Unsupervised Approach

 In the case when the number of states (or subprocesses) is very large or completely

unknown, as well as the change points between the regions, the classical approach to

segmentation focuses on a sequential detection of change points in the data by developing

a local model of the data and monitoring its dynamics for change, as for the supervised