  In this paper, we address the problem of image retrieval by learning images
representation based on the activations of a Convolutional Neural Network. We
present an end-to-end trainable network architecture that exploits a novel
multi-scale local pooling based on NetVLAD and a triplet mining procedure based
on samples difficulty to obtain an effective image representation. Extensive
experiments show that our approach is able to reach state-of-the-art results on
three standard datasets.
