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SVS-JOIN : efficient spatial visual similarity join for geo-multimedia
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Zhu, Lei, Yu, Weiren, Zhang, Chengyuan, Zhang, Zuping, Huang, Fang and Yu, Hao (2019) SVS-JOIN : efficient spatial visual similarity join for geo-multimedia. IEEE Access, 7 . pp. 158389-158408. doi:10.1109/ACCESS.2019.2948388
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WRAP-SVS-JOIN-efficient-spatial-Yu-2019.pdf - Accepted Version - Requires a PDF viewer. Download (5Mb) | Preview |
Official URL: http://dx.doi.org/10.1109/ACCESS.2019.2948388
Abstract
In the big data era, massive amount of multimedia data with geo-tags has been generated and collected by smart devices equipped with mobile communications module and position sensor module. This trend has put forward higher request on large-scale geo-multimedia retrieval. Spatial similarity join is one of the significant problems in the area of spatial database. Previous works focused on spatial textual document search problem, rather than geo-multimedia retrieval. In this paper, we investigate a novel geo-multimedia retrieval paradigm named spatial visual similarity join (SVS-JOIN for short), which aims to search similar geo-image pairs in both aspects of geo-location and visual content. Firstly, the definition of SVS-JOIN is proposed and then we present the geographical similarity and visual similarity measurement. Inspired by the approach for textual similarity join, we develop an algorithm named SVS-JOIN B by combining the PPJOIN algorithm and visual similarity. Besides, an extension of it named SVS-JOIN G is developed, which utilizes spatial grid strategy to improve the search efficiency. To further speed up the search, a novel approach called SVS-JOIN Q is carefully designed, in which a quadtree and a global inverted index are employed. Comprehensive experiments are conducted on two geo-image datasets and the results demonstrate that our solution can address the SVS-JOIN problem effectively and efficiently.
| Item Type: | Journal Article | |||||||||||||||||||||||||||
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| Subjects: | G Geography. Anthropology. Recreation > GA Mathematical geography. Cartography | |||||||||||||||||||||||||||
| Divisions: | Faculty of Science > Computer Science | |||||||||||||||||||||||||||
| Library of Congress Subject Headings (LCSH): | Cartography -- Data processing, Geography -- Network analysis, Visual analytics | |||||||||||||||||||||||||||
| Journal or Publication Title: | IEEE Access | |||||||||||||||||||||||||||
| Publisher: | IEEE | |||||||||||||||||||||||||||
| ISSN: | 2169-3536 | |||||||||||||||||||||||||||
| Official Date: | 21 October 2019 | |||||||||||||||||||||||||||
| Dates: |
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| Volume: | 7 | |||||||||||||||||||||||||||
| Page Range: | pp. 158389-158408 | |||||||||||||||||||||||||||
| DOI: | 10.1109/ACCESS.2019.2948388 | |||||||||||||||||||||||||||
| Status: | Peer Reviewed | |||||||||||||||||||||||||||
| Publication Status: | Published | |||||||||||||||||||||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||||||||||||||
| RIOXX Funder/Project Grant: |
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