Scalable top-k spatial image search on road networks

Pengpeng Zhao, Xiaopeng Kuang, Victor S. Sheng, Jiajie Xu, Jian Wu, Zhiming Cui

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations


A top-k spatial image search on road networks returns k images based on both their spatial proximity as well as the relevancy of image contents. Existing solutions for the top-k text query are not suitable to this problem since they are not sufficiently scalable to cope with hundreds of query keywords and cannot support very large road networks. In this paper, we model the problem as a top-k aggregation problem. We first propose a new separate index approach that is based on the visual vocabulary tree image index and the G-tree road network index and then propose a query processing method called an external combined algorithm(CA) method. Our experimental results demonstrate that our approach outperforms the state-of-the-art hybrid method more than one order of magnitude improvement.

Original languageEnglish
Title of host publicationDatabase Systems for Advanced Applications - 20th International Conference, DASFAA 2015, Hanoi, Vietnam, April 20-23, 2015 Proceedings, Part II
EditorsMuhammad Aamir Cheema, Matthias Renz, Cyrus Shahabi, Xiaofang Zhou
Number of pages18
ISBN (Print)9783319181226
StatePublished - 2015
Event20th International Conference on Database Systems for Advanced Applications, DASFAA 2015 - Hanoi, Viet Nam
Duration: Apr 20 2015Apr 23 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference20th International Conference on Database Systems for Advanced Applications, DASFAA 2015
Country/TerritoryViet Nam


  • Road networks
  • Separate index
  • Top-k spatial image query


Dive into the research topics of 'Scalable top-k spatial image search on road networks'. Together they form a unique fingerprint.

Cite this