Analysis of photographic aesthetics is a fast-growing and multidisciplinary field of research in computer vision. This is a timely book for a nascent field. It describes the state-of-the-art, what it has evolved from, and where it may be applied. It provides readers with a detailed understanding of the key issues in computational analysis of photographic aesthetics and describes how features relating to aesthetics, such as symmetry or leading lines, are extracted from images. It explains how techniques from the field of machine learning may be applied to learn from “big visual data” consisting of photographs with human-generated aesthetic ratings and provides a detailed comparison of human perception with state-of-the-art automated rating systems. The reader will gain an appreciation of the potential and the application of automated rating and will also be able to understand its limitations. Though fundamentally a computer science book, it draws from multiple disciplines including cognitive science, neuroscience and mathematics.