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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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Official URL: http://dx.doi.org/10.1109/ACCESS.2019.2948388

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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
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:
DateEvent
21 October 2019Published
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:
Project/Grant IDRIOXX Funder NameFunder ID
61702560[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
61379109[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
61836016[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
61972203[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
2018JJ3691Hunan Provincial Science and Technology Departmenthttp://dx.doi.org/10.13039/501100002767
2016JC2011Hunan Provincial Science and Technology Departmenthttp://dx.doi.org/10.13039/501100002767
BK20190442Natural Science Foundation of Jiangsu Provincehttp://dx.doi.org/10.13039/501100004608
2018zzts177Graduate Research and Innovation Projects of Jiangsu Provincehttp://dx.doi.org/10.13039/501100012154

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