Reverse Spatial Visual Top-$k$ Query

Abstract

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 (RSVQ k) 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 RSVQ k problem and introduce the similarity measurement. A novel hybrid index, named VR 2-Tree is designed, which is a combination of visual representation of geo-image and R-Tree. Besides, an extension of VR 2-Tree, called CVR 2-Tree is introduced and then we discuss the calculation of lower/upper bound, and then propose the optimization technique via CVR 2-Tree for further pruning. In addition, a search algorithm named RSVQ k algorithm is developed to support the efficient RSVQ k query. Comprehensive experiments are conducted on four geo-image datasets, and the results illustrate that our approach can address the RSVQ k problem effectively and efficiently. INDEX TERMS Geo-image, reverse top-k query, spatial visual query, hybrid index.

TDCMR: Triplet-Based Deep Cross-Modal Retrieval for Geo-Multimedia Data

“…Several important researches [29][30][31][32][33] are proposed in the last decade, which are support efficient LBS. Recently, some studies [2,34,35] extended spatial indexes to multimedia data, such as geo-images top-k query, k nearest neighbor query, spatial visual similarity join, and geo-image reverse query. These works make full use of geo-multimedia data via hybrid indexes to organize geo-multimedia instances through both contents and geo-locations.…”