This book commences by laving the groundwork for image classification through image feature extraction and model-driven classification in feature space. Neural networks are then introduced that can learn arbitrary models directly from labeled sample images. The behavior of a neural network is traced back to concepts of a designed classification model. We explicitly discuss relations between network topologies and their ability to represent components of a traditional designed model. It helps to gain insight into the potential and limitations of such networks.
