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Scalable Top-\( k\) Spatial Image Search on Road Networks

  • Pengpeng Zhao
  • Xiaopeng Kuang
  • Victor S. Sheng
  • Jiajie Xu
  • Jian Wu
  • Zhiming Cui
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9050)

Abstract

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.

Keywords

Top-\( k\) spatial image query Separate index Road networks 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Pengpeng Zhao
    • 1
    • 2
  • Xiaopeng Kuang
    • 1
    • 2
  • Victor S. Sheng
    • 3
  • Jiajie Xu
    • 1
    • 2
  • Jian Wu
    • 1
    • 2
  • Zhiming Cui
    • 1
    • 2
  1. 1.School of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.Collaborative Innovation Center of Novel Software Technology and IndustrializationSuzhouPeople’s Republic of China
  3. 3.Computer Science DepartmentUniversity of Central ArkansasConwayUSA

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