Learning OpenCV

Other Machine Learning Algorithms

We now have a good feel for how the ML library in OpenCV works. It is designed so that new algorithms and techniques can be implemented and embedded into the library easily. In time, it is expected that more new algorithms will appear. This section looks briefly at four machine learning routines that have recently been added to OpenCV. Each implements a well-known learning technique, by which we mean that a substantial body of literature exists on each of these methods in books, published papers, and on the Internet. For more detailed information you should consult the literature and also refer to the …/opencv/docs/ref/opencvref_ml.htm manual.

Expectation Maximization

Expectation maximization (EM) is another popular clustering technique. OpenCV supports EM only with Gaussian mixtures, but the technique itself is much more general. It involves multiple iterations of taking the most likely (average or "expected") guess given your current model and then adjusting that model to maximize its chances of being right. In OpenCV, the EM algorithm is implemented in the CvEM{} class and simply involves fitting a mixture of Gaussians to the data. Because the user provides the number of Gaussians to fit, the algorithm is similar to K-means.

K-Nearest Neighbors

One of the simplest classification techniques is K-nearest neighbors (KNN), which merely stores all the training data points. When you want to classify a new point, look up its K nearest points (for K an integer ...