Learning OpenCV

Naïve/Normal Bayes Classifier

The preceding routines are from cxcore. We'll now start discussing the machine learning (ML) library section of OpenCV. We'll begin with OpenCV's simplest supervised classifier, CvNormalBayesClassifier, which is called both a normal Bayes classifier and a naïve Bayes classifier. It's "naïve" because it assumes that all the features are independent from one another even though this is seldom the case (e.g., finding one eye usually implies that another eye is lurking nearby). Zhang discusses possible reasons for the sometimes surprisingly good performance of this classifier [Zhang04]. Naïve Bayes is not used for regression, but it's an effective classifier that can handle multiple classes, not just two. This classifier is the simplest possible case of what is now a large and growing field known as Bayesian networks, or "probabilistic graphical models". Bayesian networks are causal models; in Figure 13-6, for example, the face features in an image are caused by the existence of a face. In use, the face variable is considered a hidden variable and the face features—via image processing operations on the input image—constitute the observed evidence for the existence of a face. We call this a generative model because the face causally generates the face features. Conversely, we might start by assuming the face node is active and then randomly sample what features are probabilistically generated given that face is active.^[244] This top-down generation of data ...