基于道路网的高效连续Top- $k$地学影像搜索
Efficient Continuous Top-$k$ Geo-Image Search on Road Network
摘要：
随着移动互联网和云计算技术的快速发展，文本、图像、音频和视频已经生成、收集、存储和共享。本文提出了一种基于道路网络的连续$k$地理图像查询问题，该问题的目标是根据道路网络距离邻近度和视觉内容相似度搜索出一组地理视觉对象。现有的空间文本查询和地理图像查询方法不能有效地解决这个问题，因为它们没有考虑视觉内容的相似性和道路网络的距离接近度。为了有效地解决这一问题，本文首先提出了地理视觉对象的定义和基于道路网络的连续$k$地理视觉对象查询，并设计了一个评分函数。为了提高大规模道路网的查询效率，提出了一种基于G-Tree和可视化倒排索引技术的混合索引框架VIG-Tree的道路网地理可视化搜索算法。此外，本文还提出了安全区间和结果更新规则的概念，并在此基础上提出了一种有效的连续查询算法--移动监视器算法。在真实多媒体数据集和道路网络数据集上的实验结果表明，该方法的性能优于现有方法。
英文摘要：
With the rapid development of mobile Internet and cloud computing technology, large-scale multimedia data, e.g., texts, images, audio and videos have been generated, collected, stored and shared. In this paper, we propose a novel query problem named continuous top-$k$ geo-image query on road network which aims to search out a set of geo-visual objects based on road network distance proximity and visual content similarity. Existing approaches for spatial textual query and geo-image query cannot address this problem effectively because they do not consider both of visual content similarity and road network distance proximity on road network. In order to address this challenge effectively and efficiently, firstly we propose the definition of geo-visual objects and continuous top-$k$ geo-visual objects query on road network, then develop a score function for search. To improve the query efficiency in a large-scale road network, we propose the search algorithm named geo-visual search on road network based on a novel hybrid indexing framework called VIG-Tree, which combines G-Tree and visual inverted index technique. In addition, an important notion named safe interval and results updating rule are proposed, and based on them we develop an efficient algorithm named moving monitor algorithm to solve continuous query. Experimental evaluation on real multimedia dataset and road network dataset illustrates that our solution outperforms state-of-the-art method.
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