Chapter 10 - Representation learning and retrieval

Abstract

Recent advances in Deep Learning (DL), which inherently incorporate representation learning, have altered the landscape of representation learning tasks, such as image retrieval. This chapter aims to provide an introduction to recent deep representation learning methods, with special emphasis to the content based image retrieval task that will equip the reader with the necessary knowledge to apply these methods in practice. Particularly, we first present how a well-known DL method, known as autoencoders, can be used for representation learning tasks. Subsequently, we present a set of representative representation learning methods that aim to learn feature spaces that produce efficient retrieval-oriented representations, exploiting the geometric structure of the data in an unsupervised manner, as well as discriminative representations that exploit the class category of the data or the user's feedback using relevance feedback. Finally, we present a representation learning method suitable for retrieving objects that belong to classes that were not seen during the training process.