There are two major schools on visual learning and perception. One school is operation-oriented and learns the inferential process defined by p(W|I) directly, often in the form of an explicit transformation W ? F(I). This scheme is mostly used in supervised learning, where W is object category, and is given in training data. The other school is representation-oriented and learns the generative process p(W) and p(I|W) explicitly, then perception is to invert the generative process by maximizing or sampling p(W|I) ? p(W)p(I|W). In this scheme, p(W) may also be accounted for by a regularization term such as smoothness or sparsity. This scheme is often used in unsupervised learning where W is not available in training data.