  Due to the growth of geo-tagged images, recent web and mobile applications
provide search capabilities for images that are similar to a given query image
and simultaneously within a given geographical area. In this paper, we focus on
designing index structures to expedite these spatial-visual searches. We start
by baseline indexes that are straightforward extensions of the current popular
spatial (R*-tree) and visual (LSH) index structures. Subsequently, we propose
hybrid index structures that evaluate both spatial and visual features in
tandem. The unique challenge of this type of query is that there are
inaccuracies in both spatial and visual features. Therefore, different
traversals of the index structures may produce different images as output, some
of which more relevant to the query than the others. We compare our hybrid
structures with a set of baseline indexes in both performance and result
accuracy using three real world datasets from Flickr, Google Street View, and
GeoUGV.
