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Reverse spatial visual top-k query
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Zhu, Lei, Song, Jiayu, Yu, Weiren, Zhang, Chengyuan, Yu, Hao and Zhang, Zuping (2020) Reverse spatial visual top-k query. IEEE Access, 8 (1). pp. 21770-21787. doi:10.1109/ACCESS.2020.2968982 ISSN 2169-3536.
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Official URL: http://dx.doi.org/10.1109/ACCESS.2020.2968982
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.
| Item Type: | Journal Article | |||||||||||||||||||||||||||
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| Subjects: | G Geography. Anthropology. Recreation > G Geography (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||||||||||||||||||||
| Library of Congress Subject Headings (LCSH): | Geographic information systems , Geospatial data , Spatial data mining , Geospatial data -- Computer processing | |||||||||||||||||||||||||||
| Journal or Publication Title: | IEEE Access | |||||||||||||||||||||||||||
| Publisher: | IEEE | |||||||||||||||||||||||||||
| ISSN: | 2169-3536 | |||||||||||||||||||||||||||
| Official Date: | 23 January 2020 | |||||||||||||||||||||||||||
| Dates: |
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| Volume: | 8 | |||||||||||||||||||||||||||
| Number: | 1 | |||||||||||||||||||||||||||
| Page Range: | pp. 21770-21787 | |||||||||||||||||||||||||||
| DOI: | 10.1109/ACCESS.2020.2968982 | |||||||||||||||||||||||||||
| Status: | Peer Reviewed | |||||||||||||||||||||||||||
| Publication Status: | Published | |||||||||||||||||||||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||||||||||||||
| Date of first compliant deposit: | 11 February 2020 | |||||||||||||||||||||||||||
| Date of first compliant Open Access: | 21 February 2020 | |||||||||||||||||||||||||||
| RIOXX Funder/Project Grant: |
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