SVS-JOIN：多媒体数据的高效空间视觉相似性Join
SVS-JOIN: Efficient Spatial Visual Similarity Join over Multimedia Data
摘要：
在大数据时代，配备有移动通信模块和位置传感器模块的移动智能设备产生并收集了大量带有地理标签的多媒体数据。这一趋势对大规模地学多媒体数据的检索提出了更高的要求。空间相似性连接是空间数据库领域的重要问题之一。以前的工作集中在文本文件与地理标签，而不是地理多媒体数据，如地理图像。本文研究了一种新的搜索问题空间视觉相似性连接（SVS-JOIN），旨在寻找在地理位置和视觉内容两个方面都相似的地理图像对。首次提出了SVS-JOIN的定义，并给出了如何度量地理相似性和视觉相似性。在此基础上，本文提出了一种基于文本相似性连接的方法，并在此基础上提出了一种基于空间网格策略的文本相似性连接扩展SVS-JOIN $_G$。为了进一步提高搜索的性能，我们开发了一种新的方法称为SVS-JOIN $_Q$，它利用了四叉树和全局倒排索引。在真实地理图像数据集上的实验结果表明，该算法具有很高的性能。
英文摘要：
In the big data era, massive amount of multimedia data with geo-tags has been generated and collected by mobile smart devices equipped with mobile communications module and position sensor module. This trend has put forward higher request on large-scale of geo-multimedia data retrieval. Spatial similarity join is one of the important problem in the area of spatial database. Previous works focused on textual document with geo-tags, rather than geo-multimedia data such as geo-images. In this paper, we study a novel search problem named spatial visual similarity join (SVS-JOIN for short), which aims to find similar geo-image pairs in both the aspects of geo-location and visual content. We propose the definition of SVS-JOIN at the first time and present how to measure geographical similarity and visual similarity. Then we introduce a baseline inspired by the method for textual similarity join and a extension named SVS-JOIN$_G$ which applies spatial grid strategy to improve the efficiency. To further improve the performance of search, we develop a novel approach called SVS-JOIN$_Q$ which utilizes a quadtree and a global inverted index. Experimental evaluations on real geo-image datasets demonstrate that our solution has a really high performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
