The conditional random field (CRF) is a discriminative machine learning algorithm introduced by John Lafferty, Andrew McCallum, and Fernando Pereira [7:9] at the turn of the century as an alternative to the HMM. The algorithm was originally developed to assign labels to a set of observation sequences.
Let's consider a concrete example to understand the conditional relation between the observations and the label data.
Let's consider the problem of detecting a foul during a soccer game using a combination of video and audio. The objective is to assist the referee and analyze the behavior of the players to determine whether an action on the field is dangerous (red card), inappropriate (yellow card), in doubt to be replayed, or legitimate. The following image is an example of the segmentation of a video frame for image processing:
An example of an image processing problem requiring machine learning