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With the wide application of mobile Internet techniques an location-based services (LBS), massive multimedia data with geo-tags has been generated and collected. In this paper, we investigate a novel type of spatial query problem, named reverse spatial visual top-k query (RSVQk) that aims to retrieve a set of geo-images that have the query as one of the most relevant geo-images in both geographical proximity and visual similarity. Existing approaches for reverse top-k queries are not suitable to address this problem because they cannot effectively process unstructured data, such as image. To this end, firstly we propose the definition of RSVQk problem and introduce the similarity measurement. A novel hybrid index, named VR2-Tree is designed, which is a combination of visual representation of geo-image and R-Tree. Besides, an extension of VR2-Tree, called CVR2-Tree is introduced and then we discuss the calculation of lower/upper bound, and then propose the optimization technique via CVR2-Tree for further pruning. In addition, a search algorithm named RSVQk algorithm is developed to support the efficient RSVQk query. Comprehensive experiments are conducted on four geo-image datasets, and the results illustrate that our approach can address the RSVQk problem effectively and efficiently.

Reverse Spatial Visual Top- $k$ Query