The first part of this tutorial, dedicated to large-scale image retrieval, will first introduce the typical use-cases and the datasets used for evaluation of image search when considering an unsupervised framework. We will present different classes of techniques considering different trade-offs with respect to efficiency and search quality. Starting with the most costly but precise patch-based matching and spatial verification techniques, we will present the bag-of-words model, its matching interpretation and several improvements, including re-ranking techniques based on spatial verification and query expansion. Finally, the most scalable techniques based on aggregation/coding techniques and compressed-domain search will be detailed.