  The current models of image representation based on Convolutional Neural
Networks (CNN) have shown tremendous performance in image retrieval. Such
models are inspired by the information flow along the visual pathway in the
human visual cortex. We propose that in the field of particular object
retrieval, the process of extracting CNN representations from query images with
a given region of interest (ROI) can also be modelled by taking inspiration
from human vision. Particularly, we show that by making the CNN pay attention
on the ROI while extracting query image representation leads to significant
improvement over the baseline methods on challenging Oxford5k and Paris6k
datasets. Furthermore, we propose an extension to a recently introduced
encoding method for CNN representations, regional maximum activations of
convolutions (R-MAC). The proposed extension weights the regional
representations using a novel saliency measure prior to aggregation. This leads
to further improvement in retrieval accuracy.
