Classification

Classification or cluster analysis groups similar objects in distinct clusters (in the EM Field usually called classes) while minimizing the variance (of a certain parameter) within one cluster/class. During image processing, classification is used to find similar 2D images within a dataset, but also similar 3D structures within a set of structures.

A prerequisite for a feasible classification is a complexity reduction of the given input dataset. This can be achieved by performing a Principal Component Analysis (PCA). The PCA can be carried out either internally by the classification-logic or beforehand by the respective PCA logic. Additionally, the user has to define the input set and the number of expected classes/clusters. Now, the logic will split the dataset according to the information provided by the PCA into as many classes/clusters as determined aiming for minimal variance within each class/cluster.

This classification mode uses an internal/external PCA to reduce the dataset's complexity before splitting it into a defined number of classes.

Parameters	Description
Eigen images location	Define, whether the Eigen images used for complexity reduction are generated on-the-fly internally (intern) or provided by an input from en external source (extern)
→ Number of eigen images	How many Eigen images (and therefore dimensions) should be used as components during linear combination
Split up method	Determine, whether large classes should be slip up into smaller classes due to their number of containing objects (size) or due to their high internal variance of the cross-correlation-coefficients (cccVariance)
Number of classes	Number of resulting classes/clusters
Remove duplicated images	???

Once clusters/classes of images are found, they can be averaged (see SumByClassNumber) to improve the signal-to-noise-ration substantially.¹⁾