  Cross-modal retrieval aims to measure the content similarity between
different types of data. The idea has been previously applied to visual, text,
and speech data. In this paper, we present a novel cross-modal retrieval method
specifically for multi-view images, called Cross-view Image Retrieval CVIR. Our
approach aims to find a feature space as well as an embedding space in which
samples from street-view images are compared directly to satellite-view images
(and vice-versa). For this comparison, a novel deep metric learning based
solution "DeepCVIR" has been proposed. Previous cross-view image datasets are
deficient in that they (1) lack class information; (2) were originally
collected for cross-view image geolocalization task with coupled images; (3) do
not include any images from off-street locations. To train, compare, and
evaluate the performance of cross-view image retrieval, we present a new 6
class cross-view image dataset termed as CrossViewRet which comprises of images
including freeway, mountain, palace, river, ship, and stadium with 700
high-resolution dual-view images for each class. Results show that the proposed
DeepCVIR outperforms conventional matching approaches on the CVIR task for the
given dataset and would also serve as the baseline for future research.
