SVS-JOIN: Efficient Spatial Visual Similarity Join over Multimedia Data

TL;DR

This paper introduces SVS-JOIN, a novel method for efficiently performing spatial visual similarity joins on geo-multimedia data, combining geo-location and visual content analysis to improve large-scale geo-image retrieval.

Contribution

It defines the spatial visual similarity join problem and proposes three methods, including a novel quadtree and inverted index-based approach, to enhance efficiency.

Findings

SVS-JOIN$_G$ improves efficiency with spatial grid strategy.

SVS-JOIN$_Q$ achieves high performance using quadtree and inverted index.

Experimental results show significant performance gains.

Abstract

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.