Massive amounts of geo-tagged and textually annotated images are provided by online photo services such as Flickr and Zommr. However, most existing image retrieval engines only consider text annotations. We present I2RS, a system that allows users to view geo-textual images on Google Maps, find hot topics within a specific geographic region and time period, retrieve images similar to a query image, and receive recommended images that they might be interested in. I2RS is a distributed geo-textual  i mage  r etrieval and  r ecommendation  s ystem that employs SPB-trees to index geo-textual images, and that utilizes metric similarity queries, including top-m spatio-temporal range and k nearest neighbor queries, to support geo-textual image retrieval and recommendation. The system adopts the browser-server model, whereas the server is deployed in a distributed environment that enables efficiency and scalability to huge amounts of data and requests. A rich set of 100 million geo-textual images crawled from Flickr is used to demonstrate that, I2RS can return high-quality answers in an interactive way and support efficient updates for high image arrival rates.
['Lu Chen', 'Yunjun Gao', 'Zhihao Xing', 'Christian S. Jensen', 'Gang Chen']
I2RS: a distributed geo-textual image retrieval and recommendation system