  The recent advances brought by deep learning allowed to improve the
performance in image retrieval tasks. Through the many convolutional layers,
available in a Convolutional Neural Network (CNN), it is possible to obtain a
hierarchy of features from the evaluated image. At every step, the patches
extracted are smaller than the previous levels and more representative.
Following this idea, this paper introduces a new detector applied on the
feature maps extracted from pre-trained CNN. Specifically, this approach lets
to increase the number of features in order to increase the performance of the
aggregation algorithms like the most famous and used VLAD embedding. The
proposed approach is tested on different public datasets: Holidays, Oxford5k,
Paris6k and UKB.
