In this chapter, we look at a wide range of feature learning architectures and deep learning architectures, which incorporate a range of feature models and classification models. This chapter digs deeper into the background concepts of feature learning and artificial neural networks summarized in the taxonomy of Chap. 9 , and complements the local and regional feature descriptor surveys in Chaps. 3 , 4 , 5 , and 6 . The architectures in the survey represent significant variations across neural-network approaches, local feature descriptor and classification-based approaches, and ensemble approaches. The architecture taken together as the sum of its parts is apparently more important than individual parts or components of the design, such as the choice of feature descriptor, number of levels in the feature hierarchy, number of features per layer, or the choice of classifier. Good results are being reported across a wide range of architectures.