  Automatic image-based food recognition is a particularly challenging task.
Traditional image analysis approaches have achieved low classification accuracy
in the past, whereas deep learning approaches enabled the identification of
food types and their ingredients. The contents of food dishes are typically
deformable objects, usually including complex semantics, which makes the task
of defining their structure very difficult. Deep learning methods have already
shown very promising results in such challenges, so this chapter focuses on the
presentation of some popular approaches and techniques applied in image-based
food recognition. The three main lines of solutions, namely the design from
scratch, the transfer learning and the platform-based approaches, are outlined,
particularly for the task at hand, and are tested and compared to reveal the
inherent strengths and weaknesses. The chapter is complemented with basic
background material, a section devoted to the relevant datasets that are
crucial in light of the empirical approaches adopted, and some concluding
remarks that underline the future directions.
