  In the big data era, massive amount of multimedia data with geo-tags has been
generated and collected by mobile smart devices equipped with mobile
communications module and position sensor module. This trend has put forward
higher request on large-scale of geo-multimedia data retrieval. Spatial
similarity join is one of the important problem in the area of spatial
database. Previous works focused on textual document with geo-tags, rather than
geo-multimedia data such as geo-images. In this paper, we study a novel search
problem named spatial visual similarity join (SVS-JOIN for short), which aims
to find similar geo-image pairs in both the aspects of geo-location and visual
content. We propose the definition of SVS-JOIN at the first time and present
how to measure geographical similarity and visual similarity. Then we introduce
a baseline inspired by the method for textual similarity join and a extension
named SVS-JOIN$_G$ which applies spatial grid strategy to improve the
efficiency. To further improve the performance of search, we develop a novel
approach called SVS-JOIN$_Q$ which utilizes a quadtree and a global inverted
index. Experimental evaluations on real geo-image datasets demonstrate that our
solution has a really high performance.
