showed that ImageNet pre-trained CNNs provide very good descriptors, it is very hard to find a large-enough database where state of the art performance is not related with CNNs. As such, the bulk of our experiments revolve, in one way or another, around the CNN algorithms, either as direct classification methods or as auxiliary tools.

The first network architecture tested in this work is AlexNet, proposed by Krizhevsky et al [-@Krizhevsky:12]. This architecture features 5 convolutional layers followed by 3 fully connected layers. The size of the receptive fields is large ($11 \times 11$), in comparison to more recent architectures; the design is optimized for the use of two parallel GPUs, although currently it easily fits on a single GPU. Even though it has been visibly surpassed by other networks, it still remains a main reference point in all architecture comparisons.

A more powerful architecture is represented by the VGG-type networks, proposed by Simonyan and Zisserman [-@Simonyan2014]. In contrast to AlexNet, this design features smaller receptive fields ($3 \times 3$), but with a stride between regions of only 1. These decisions have led to the possibility of increased depth, by stacking sequentially up to 5 convolutional layers, and, subsequently, to 