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.
@article{arxiv.2004.09695,
title = {Image Retrieval using Multi-scale CNN Features Pooling},
author = {Federico Vaccaro and Marco Bertini and Tiberio Uricchio and Alberto Del Bimbo},
journal= {arXiv preprint arXiv:2004.09695},
year = {2020}
}