  The landmark recognition problem is far from being solved, but with the use
of features extracted from intermediate layers of Convolutional Neural Networks
(CNNs), excellent results have been obtained. In this work, we propose some
improvements on the creation of R-MAC descriptors in order to make the
newly-proposed R-MAC+ descriptors more representative than the previous ones.
However, the main contribution of this paper is a novel retrieval technique,
that exploits the fine representativeness of the MAC descriptors of the
database images. Using this descriptors called "db regions" during the
retrieval stage, the performance is greatly improved. The proposed method is
tested on different public datasets: Oxford5k, Paris6k and Holidays. It
outperforms the state-of-the- art results on Holidays and reached excellent
results on Oxford5k and Paris6k, overcame only by approaches based on
fine-tuning strategies.
