Scalable Top- $$ k$$ Spatial Image Search on Road Networks

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• 作者：Pengpeng Zhao (17) (18)
  Xiaopeng Kuang (17) (18)
  Victor S. Sheng (19)
  Jiajie Xu (17) (18)
  Jian Wu (17) (18)
  Zhiming Cui (17) (18)
  
  17. School of Computer Science and Technology ; Soochow University ; Suzhou ; China
  18. Collaborative Innovation Center of Novel Software Technology and Industrialization ; Suzhou ; 215006 ; People鈥檚 Republic of China
  19. Computer Science Department ; University of Central Arkansas ; Conway ; USA
  
• 关键词：Top ; $$ k$$ spatial image query ; Separate index ; Road networks
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9050
• 期：1
• 页码：379-396
• 全文大小：838 KB
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• 作者单位：Database Systems for Advanced Applications
• 丛书名：978-3-319-18122-6
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

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.