1 research outputs found
Saliency deep embedding for aurora image search
Deep neural networks have achieved remarkable success in the field of image
search. However, the state-of-the-art algorithms are trained and tested for
natural images captured with ordinary cameras. In this paper, we aim to explore
a new search method for images captured with circular fisheye lens, especially
the aurora images. To reduce the interference from uninformative regions and
focus on the most interested regions, we propose a saliency proposal network
(SPN) to replace the region proposal network (RPN) in the recent Mask R-CNN. In
our SPN, the centers of the anchors are not distributed in a rectangular
meshing manner, but exhibit spherical distortion. Additionally, the directions
of the anchors are along the deformation lines perpendicular to the magnetic
meridian, which perfectly accords with the imaging principle of circular
fisheye lens. Extensive experiments are performed on the big aurora data,
demonstrating the superiority of our method in both search accuracy and
efficiency