  The pixels in an image, and the objects, scenes, and actions that they
compose, determine whether an image will be memorable or forgettable. While
memorability varies by image, it is largely independent of an individual
observer. Observer independence is what makes memorability an image-computable
measure of information, and eligible for automatic prediction. In this chapter,
we zoom into memorability with a computational lens, detailing the
state-of-the-art algorithms that accurately predict image memorability relative
to human behavioral data, using image features at different scales from raw
pixels to semantic labels. We discuss the design of algorithms and
visualizations for face, object, and scene memorability, as well as algorithms
that generalize beyond static scenes to actions and videos. We cover the
state-of-the-art deep learning approaches that are the current front runners in
the memorability prediction space. Beyond prediction, we show how recent A.I.
approaches can be used to create and modify visual memorability. Finally, we
preview the computational applications that memorability can power, from
filtering visual streams to enhancing augmented reality interfaces.
